├── README.md ├── calibration.txt ├── config_files_gc ├── tartanvo │ ├── process.py │ └── undistort.py ├── vinsmono │ └── groundchallenge.yaml ├── vinsrgbd │ ├── groundchallenge_config.yaml │ └── groundchallenge_depth_config.yaml └── viwfusion │ ├── groundchallenge.yaml │ └── groundchallenge_cam.yaml ├── fig ├── corridor.jpg ├── ground-challenge.pptx ├── hall.jpg ├── loop.jpg ├── office.jpg ├── robot.jpg ├── robot_org.jpg ├── room.jpg ├── roughroad.jpg ├── scenarios.jpg ├── static.jpg └── wall.jpg └── psudo_gt ├── corridor1.txt ├── corridor2.txt ├── darkroom1.txt ├── darkroom2.txt ├── darkroom3.txt ├── hall1.txt ├── hall2.txt ├── hall3.txt ├── loop1.txt ├── loop2.txt ├── motionblur1.txt ├── motionblur2.txt ├── motionblur3.txt ├── occlusion1.txt ├── occlusion2.txt ├── occlusion3.txt ├── occlusion4.txt ├── office1.txt ├── office2.txt ├── office3.txt ├── room1.txt ├── room2.txt ├── room3.txt ├── rotation1.txt ├── rotation2.txt ├── rotation3.txt ├── roughroad1.txt ├── roughroad2.txt ├── roughroad3.txt ├── slope1.txt ├── slope2.txt ├── static1.txt ├── static2.txt ├── wall1.txt ├── wall2.txt └── wall3.txt /README.md: -------------------------------------------------------------------------------- 1 | 2 | # Ground-Challenge 3 | ## A Multi-sensor SLAM Dataset Focusing on Corner Cases for Ground Robots 4 | 5 | 6 | 7 |
8 | 9 | [![Author](https://img.shields.io/badge/Author-Jie%20Yin-blue)](https://sjtuyinjie.github.io/) 10 | [![Paper](https://img.shields.io/badge/Paper-GroundChallenge-yellow)](https://ieeexplore.ieee.org/document/10354969/) 11 | [![Preprint](https://img.shields.io/badge/Preprint-Arxiv-purple)](https://arxiv.org/abs/2307.03890) 12 | [![License](https://img.shields.io/badge/License-MIT-cyan)]() 13 | [![News](https://img.shields.io/badge/News-orange)](https://mp.weixin.qq.com/s/jnfVFOXX6aiqGBX6pVdgIg) 14 | 15 | 16 | 17 |
18 |

Figure 1. Different corner cases for SLAM

19 | 20 | 21 | ## Notice: 22 | ### We strongly recommend that the newly proposed SLAM algorithm be tested on our Ground-Challenge benchmark, because our data has following features: 23 | 24 | 1. A rich pool of sensory information including RGBD, wheel, IMU and so on. 25 | 26 | 2. This benchmark includes diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. 27 | 28 | 29 | 3. This benchmark brings great challenge to existing cutting-edge SLAM algorithms including VINS-Mono, [ORB-SLAM3](https://github.com/UZ-SLAMLab/ORB_SLAM3), [VINS-RGBD](https://github.com/STAR-Center/VINS-RGBD), [VIW-Fusion](https://github.com/TouchDeeper/VIW-Fusion) and [TartanVO](https://github.com/castacks/tartanvo). If your proposed algorihm outperforms SOTA systems on this dataset, your paper will be much more convincing and valuable. 30 | 31 | 32 | 33 | ## License 34 | 35 | The paper link is [here](https://arxiv.org/abs/2307.03890).If you use Ground-Challenge in an academic work, please cite: 36 | ~~~ 37 | @inproceedings{yin2023ground, 38 | title={Ground-challenge: A multi-sensor slam dataset focusing on corner cases for ground robots}, 39 | author={Yin, Jie and Yin, Hao and Liang, Conghui and Jiang, Haitao and Zhang, Zhengyou}, 40 | booktitle={2023 IEEE International Conference on Robotics and Biomimetics (ROBIO)}, 41 | pages={1--5}, 42 | year={2023}, 43 | organization={IEEE} 44 | } 45 | ~~~ 46 | 47 | ## ABSTRACT: 48 | 49 | We introduce Ground-Challenge: a novel dataset collected by a ground robot with multiple sensors including an RGB-D camera, an inertial measurement unit (IMU), a wheel odometer and a 3D LiDAR to support the research on corner cases of visual SLAM systems. 50 | Our dataset comprises 36 trajectories with diverse corner cases such as aggressive motion, severe occlusion, changing illumination, few textures, pure rotation, motion blur, wheel suspension, etc. Some state-of-the-art SLAM algorithms are tested on our dataset, showing that these systems are seriously drifting and even failing on specific sequences. 51 | We will release the dataset and relevant materials upon paper publication to benefit the research community. 52 | 53 | 54 | 55 | 56 | 57 | 58 | 59 | 60 | 61 | 62 | ## 1.SENSOR SETUP 63 | ### 1.1 Acquisition Platform 64 | The ground robot is given below. The unit of the figures is centimeter. 65 | 66 |
67 | 68 |
69 | 70 |

Figure 2. The data capture robot.

71 | 72 | 73 | 74 | 75 | 76 | 77 | 78 | 79 | 80 | ### 1.2 Sensor parameters 81 | 82 | All the sensors and track devices and their most important parameters are listed as below: 83 | 84 | * **LIDAR** Velodyne VLP-16, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,10Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°. 85 | 86 | * **V-I Sensor**,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz 87 | * **IMU**,Xsens Mti-300,9-axis,400Hz; 88 | * **Wheel Odometer**,AgileX,2D,25Hz; 89 | 90 | The rostopics of our rosbag sequences are listed as follows: 91 | 92 | * LIDAR: `/velodyne_points` 93 | 94 | * V-I Sensor: 95 | `/camera/color/image_raw `, 96 | `/camera/depth/image_raw `, 97 | `/camera/imu` 98 | 99 | * IMU: `/imu/data ` 100 | 101 | * Wheel Odometer: `/odom ` 102 | 103 | ## 2.DATASET SEQUENCES 104 | 105 | 106 | > [!TIP] 107 | > We are delighted to see that many users enjoy our Ground-Challenge dataset. However, **due to an overwhelming number of download requests, OneDrive has become unstable. If you encounter any issues, please wait for about 10 minutes and try again.** 108 | 109 | 110 | 111 | An overview of Ground-Challenge is given in the table below: 112 | Scenario|Darkroom|Occlusion|Office|Room|Wall|Motionblur|Hall|Loop|Roughroad|Corridor|Rotation|Static|Slope|TOTAL 113 | --|:--|:--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--:|--: 114 | Number |3|4|3|3|3|3|3|2|3|2|3|2|2|36 115 | Dist/m |92.0|273.8|75.5|102.1|86.7|166.6|236.3|371.8|68.1|164.3|12.4|1.9|128.5|1780.0 116 | Duration/s |203.6|334.2|164.0|154.7|189.3|145.5|302.4|332.7|186.3|198.1|183.2|92.6|195.0|2681.6 117 | Size/GB|6.1|9.9|4.7|4.6|5.6|4.3|8.7|9.9|5.4|5.8|5.4|2.7|5.7|78.8 118 | 119 | 120 | ### 2.1 Visual Challenges 121 | 122 |
123 | 124 | 125 | Sequence Name|Total Size|Duration|Features|Rosbag 126 | --|:--|:--:|--:|--: 127 | Darkroom1|2.9g|100s|slight light, going into a room|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfJ_m0MD1upOphD5vnCoqSoBJ54_5D_vQQD0hpAwAq0qPg?e=1tmbgq) 128 | Darkroom2|2.3g|76s|sharp turn|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EU4Ns3qCDO5PkjByuoANpf4BRWksqGt_pDnEnKXbbdz9Sw?e=AX5bp4) 129 | Darkroom3|1.9g|64s|slight light|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EU2tnWCQitJOiRmNeHaCmtEBG2HssIMag9DRUrJOmYYxUQ?e=2dXfFr) 130 | Occlusion1|2.9g|97s|moving feet, far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EfpLxjCNp8ZHvpFEnItwvhABQbwCsvcr1kmUtsnpM4fqiw?e=tA6KpT) 131 | Occlusion2|3.2g|108s|hand occlusion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaAZkIssBohOkjpCyUzx_-8BrngUTuuOyJVz5IPGqdEhQg?e=Sv8uQi) 132 | Occlusion3|2.6g|89s|hand occlusion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ef9vHhDNZh5Drk5dwzL3dLgBX9-_1XkQxqrIpOHR7C57aw?e=FeA8Yo) 133 | Occlusion4|1.2g|40s|complete occlusion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EVFiMlWRhydNukCylXGganoB_w-DJ-7PzBB7Y_cXiWvBGw?e=WZtsNP) 134 | Office1|1.3g|46s|exposure change|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaBHPzN4EuBOu-X-l52sfQMBnsjYnksgxDbWVzOMy-sxIQ?e=splygr) 135 | Office2|1.9g|66s|going into a dark room|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EcBQ7EME7JNKuXpcyoitWbcBc_okPBGVl_LJhUhtRF7lyg?e=G7FgMx) 136 | Office3|1.5g|52s|office|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EVcZ-4q2s_RPqcpIX2LM70EBvUlGw9O8eiHzDjIadLko6A?e=NIf9jb) 137 | Room1|1.3g|46s|exposure change|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EcrkgZ7gyv1KnpNWajEXzNIBFaJOZzODtIxtMSWgm45RxA?e=zAZlRJ) 138 | Room2|1.9g|66s|going into a dark room|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EeHeiCiRCUVAj8pyOTAO0NcBfdhO_BQJUeUNMGfLTKXcbg?e=lLLkkp) 139 | Room3|1.5g|52s|office|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Eek7te0hlThHpdU61IdMWZsBxv4Sty-7ofFAFQZRa9HGPA?e=WWU85c) 140 | Motionblur1|1.5g|52s|aggressive motion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EVGUofZ_fd1IugqiyFQcV94BVL5qTyNA7HTOkZJNDKTiVw?e=CsRWmn) 141 | Motionblur2|1.6g|54s|aggressive motion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EeplJdHLC9tMkbN3rXqvc0oBuw5CaRlR7EuxYEBrNnJ9ig?e=lGybNv) 142 | Motionblur3|1.2g|40s|aggressive motion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EWMqyAlMP8hHmHTab36MkLIB3C_0X6N0n7ARrdSoPBvm1g?e=MBMXbJ) 143 | Wall1|1.7g|59s|wall in a corridor|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EU15i59tJHxHgBmWZZwvMMkBaNVA37ulgy_Hrk8mP7jyOg?e=6c6hn7) 144 | Wall2|2.0g|66s|wall in a big hall|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EcHTwIhL6Q9Ao9UEtYknrLgBRxK_AyToEPwyfZNrt2PDMg?e=AzPYe4) 145 | Wall3|3.9g|65s|wall in a corridor|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ERaf7TV8R2hBljLdGOIRnRQBGwba8YPYoaJrp4WvytByog?e=k175KG) 146 |
147 | 148 | ### 2.2 Wheel Challenge 149 | 150 |
151 | 152 | Sequence Name|Total Size|Duration|Features|Rosbag 153 | --|:--|:--:|--:|--: 154 | Hall1|2.6g|91s|slippery ground, a reflective surface|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EbRe8o9pakFCvmjSKEgOEc0BglOJgScs0IbHFcxr84Fxig?e=WjkQQM) 155 | Hall2|3.2g|110s|slippery ground, a reflective surface|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EYSOrLJ0yRdLrUwH2HIej0kB53Lnwf7TuouMDO-TY-Pkrw?e=dlwBMs) 156 | Hall3|2.9g|101s|slippery ground, walking human|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ES5vEb2oe1hAqMVQY3LfgqMBXPlNazdu9B__0R0j3cwyhg?e=yTNLH7) 157 | Loop1|4.1g|97s|moving feet, far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EU9mhOpXfEJBm-M0cwbhTwsBL_WvjwkO-GcTzPnmwa-OkQ?e=qPPqGR) 158 | Loop2|5.8g|137s|hand occlusion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ER_ZcyUZN3dNiwkxT-NeqEoBbphpoCbLnsf5ARp52ARdjA?e=Z3quXF) 159 | Roughroad1|2.2g|75s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EX-Yic1OhN1GjrcGoaE2mu0BTua8w3C8PvRzMa0rsm84pQ?e=fb4Kb4) 160 | Roughroad2|1.5g|52s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EV-gMKCrnbFFmGLU5FV32loB-OMys7O3dZ25InJR0aH-0Q?e=Xp8Fmy) 161 | Roughroad3|1.8g|59s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EcJemetJZMBPnJwlXNwCySkBTGhc5QXRlTo0fOEFF0uWfQ?e=KuX3EH) 162 | 163 |
164 | 165 | 166 | 167 | 168 | 169 | 170 | ### 2.3 Specific Movement Patterns 171 |
172 | 173 | Sequence Name|Total Size|Duration|Features|Rosbag 174 | --|:--|:--:|--:|--: 175 | Corridor1|2.9g|100s|zigzag, long corridor|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ed4KpGMgT2RMgzqt0HU0fw8BOvtgx18YnkdJQVekzwfe8g?e=5S9uLK) 176 | Corridor2|2.9g|98s|straight forward, long corridor|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EcWKsukr7nNLrqKloXY3UAIBKakA6ppET_SGJZT-VCYO5Q?e=Ccp0fd) 177 | Rotation1|1.6g|53s|moving feet, far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EeYXHDYMNYNDoMocs1YBj34B3TvYSQtDqV8JjAel37uL9Q?e=upMCTa) 178 | Rotation2|2.1g|73s|hand occlusion|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ET-YIfqH2n1Fv0mlx2nO3QEBScAuV01IGn_GqgTQyHFcpA?e=X9zdoB) 179 | Rotation3|1.7g|57s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EX1DtN9we2lDlhTPkG4NaZsBSyOkPGuqfgJxz2o0WwxjFQ?e=EBweG6) 180 | Static1|1.6g|56s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EbAEddIfZqhFglNNf5urVmcB9lFue_HuhJ1_bxwNnxvCGQ?e=eCSfal) 181 | Static2|1.1g|37s|rough road|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUy-egIJ3vVMu0Kpc8Q92AcB8UU6x_FvogsvFrclhIme2g?e=WtYGQl) 182 | Slope1|2.8g|96s|slope|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ETR6Sbd7PltEvzggPaa0DdoBwgo3PbsUWNjwBTnckX5MTA?e=sgfRI5) 183 | Slope2|2.9g|99s|slope|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQ6TUsSfrBdAiAljY8o2HLEBj62PzAp2g0c4XKcQtvHg0A?e=iKrk3m) 184 |
185 | 186 | ## 3. Configuration Files 187 | We provide configuration files for several cutting-edge baseline methods, including [VINS-RGBD](https://github.com/sjtuyinjie/Ground-Challenge/tree/main/config_files_gc/vinsrgbd),[TartanVO](https://github.com/sjtuyinjie/Ground-Challenge/tree/main/config_files_gc/tartanvo),[VINS-Mono](https://github.com/sjtuyinjie/Ground-Challenge/tree/main/config_files_gc/vinsmono) and [VIW-Fusion](https://github.com/sjtuyinjie/Ground-Challenge/tree/main/config_files_gc/viwfusion). 188 | 189 | 190 | ## Star History 191 | 192 | [![Star History Chart](https://api.star-history.com/svg?repos=sjtuyinjie/Ground-Challenge&type=Timeline)](https://star-history.com/#Ashutosh00710/github-readme-activity-graph&Timeline) 193 | 194 | -------------------------------------------------------------------------------- /calibration.txt: -------------------------------------------------------------------------------- 1 | model_type: PINHOLE 2 | image_width: 640 3 | image_height: 480 4 | distortion_parameters: 5 | k1: 1.4865749308203452e-01 6 | k2: -4.6815685578576460e-01 7 | p1: 1.6205585303208318e-03 8 | p2: -8.9101576735577930e-03 9 | projection_parameters: 10 | fx: 6.2097277909374247e+02 11 | fy: 6.2212293397677581e+02 12 | cx: 3.1175896455154810e+02 13 | cy: 2.4718077836114819e+02 14 | 15 | 16 | 17 | body_T_cam0: !!opencv-matrix 18 | rows: 4 19 | cols: 4 20 | dt: d 21 | data: [ 0.99957087 , 0.00215313 , 0.02921355 , 0.03668114, 22 | -0.00192891 ,0.99996848, -0.00770122 ,-0.00477653, 23 | -0.02922921 , 0.00764156 ,0.99954353 , 0.0316039, 24 | 0. , 0. , 0. , 1. ] 25 | 26 | 27 | body_T_wheel: !!opencv-matrix 28 | rows: 4 29 | cols: 4 30 | dt: d 31 | data: [-0.0424561 , -0.998603 ,-0.0314461,0.0283756, 32 | 0.0729004 , 0.0282942, -0.996938,0.159482, 33 | 0.996435 ,-0.0446186 , 0.0715973,-0.136109 , 34 | 0, 0, 0, 1] -------------------------------------------------------------------------------- /config_files_gc/tartanvo/process.py: -------------------------------------------------------------------------------- 1 | #coding:utf-8 2 | # 假设pose.txt和time.txt文件已经存在,下面是读取这两个文件,然后合并它们内容的Python脚本示例。 3 | 4 | # 首先定义pose.txt和time.txt的路径 5 | name='corridor1' 6 | pose_file_path = name+'_pose.txt' 7 | time_file_path = name+'_time.txt' 8 | result_file_path = name+'.txt' 9 | 10 | # 读取pose.txt文件 11 | with open(pose_file_path, 'r') as pose_file: 12 | pose_lines = pose_file.readlines() 13 | 14 | # 读取time.txt文件 15 | with open(time_file_path, 'r') as time_file: 16 | time_lines = time_file.readlines() 17 | 18 | # 合并时间到pose行,并准备写入新文件 19 | result_lines = [] 20 | for pose_line, time_line in zip(pose_lines, time_lines): 21 | time = time_line.split(',')[1].strip() # 提取时间数据 22 | result_line = f"{time} {pose_line.strip()}\n" # 合并时间和pose数据 23 | result_lines.append(result_line) 24 | 25 | # 写入结果到result.txt 26 | with open(result_file_path, 'w') as result_file: 27 | result_file.writelines(result_lines) 28 | 29 | result_file_path # 输出结果文件的路径以供下载或检查 30 | -------------------------------------------------------------------------------- /config_files_gc/tartanvo/undistort.py: -------------------------------------------------------------------------------- 1 | # coding:utf-8 2 | # !/usr/bin/python 3 | 4 | # Extract images from a bag file. 5 | 6 | # PKG = 'beginner_tutorials' 7 | import roslib # roslib.load_manifest(PKG) 8 | import rosbag 9 | import rospy 10 | import cv2 11 | import numpy as np 12 | import os 13 | from sensor_msgs.msg import Image 14 | from cv_bridge import CvBridge 15 | from cv_bridge import CvBridgeError 16 | from matplotlib import pyplot as plt 17 | # Reading bag filename from command line or roslaunch parameter. 18 | # import os 19 | import sys 20 | 21 | 22 | 23 | path='/media/car/YJ06/ground-challenge/bag/' # bag包路径, /结尾 24 | filename='loop2' 25 | topic_names={'rgb_topics':['/camera/color/image_raw'], 'depth_topics':[]} 26 | # cameras=['camera1','camera2'] 27 | 28 | 29 | cam_rgb_path=[path+'/rgbd/'+filename+'/rgb/'] 30 | cam_depth_path=[path+'/rgbd/'+filename+'/depth/'] 31 | 32 | # for camPath in cam_rgb_path: 33 | # print(camPath) 34 | if os.path.exists(path+filename+'.bag'): 35 | for path_dir in cam_rgb_path: 36 | if not os.path.exists(path_dir): 37 | os.makedirs(path_dir) 38 | for path_dir in cam_depth_path: 39 | if not os.path.exists(path_dir): 40 | os.makedirs(path_dir) 41 | else: 42 | print('bag file not exist!!') 43 | sys.exit() 44 | 45 | file=open(cam_rgb_path[0]+"/time.txt", "w") 46 | print(cam_rgb_path[0]+"/time.txt") 47 | 48 | # 相机内参和畸变系数 49 | camera_matrix = np.array([[620.97277909374247, 0, 311.75896455154810], 50 | [0, 622.12293397677581, 247.18077836114819], 51 | [0, 0, 1]]) 52 | dist_coeffs = np.array([0.14865749308203452, -0.46815685578576460, 0.0016205585303208318, -0.0089101576735577930, 0]) 53 | seqnum=0 54 | class ImageCreator(): 55 | def __init__(self): 56 | self.bridge = CvBridge() 57 | print("Waiting......") 58 | 59 | global seqnum 60 | 61 | with rosbag.Bag(path+filename+'.bag','r') as bag: # 要读取的bag文件; 62 | for topic, msg, t in bag.read_messages(): 63 | for i in range(len(topic_names['rgb_topics'])): 64 | if topic == topic_names['rgb_topics'][i]: # 图像的topic; 65 | try: 66 | cv_image = self.bridge.imgmsg_to_cv2(msg, 'bgr8') 67 | cv_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2GRAY) # 转换为灰度图 68 | # 应用去畸变 69 | undistorted_image = cv2.undistort(cv_image, camera_matrix, dist_coeffs) 70 | except CvBridgeError as e: 71 | print(e) 72 | timestr = "%.9f" % msg.header.stamp.to_sec() 73 | image_name = "{:06d}.png".format(seqnum) 74 | seqnum += 1 75 | # 保存去畸变后的图像 76 | cv2.imwrite(cam_rgb_path[i] + image_name, undistorted_image) 77 | seqstr='' 78 | if(seqnum<10): 79 | seqstr="00000"+str(seqnum) 80 | elif (seqnum<100): 81 | seqstr="0000"+str(seqnum) 82 | elif (seqnum<1000): 83 | seqstr="000"+str(seqnum) 84 | elif (seqnum<10000): 85 | seqstr="00"+str(seqnum) 86 | elif (seqnum<100000): 87 | seqstr="0"+str(seqnum) 88 | # with open(cam_rgb_path[0]+"/time.txt", "w")as timestamp_file_rgb: 89 | file=open(cam_rgb_path[0]+"/time.txt", "a") 90 | 91 | file.write(seqstr+','+timestr+"\n") 92 | 93 | 94 | 95 | 96 | file.close() 97 | 98 | if __name__ == '__main__': 99 | # rospy.init_node(PKG) 100 | try: 101 | image_creator = ImageCreator() 102 | print("done") 103 | except rospy.ROSInterruptException: 104 | pass -------------------------------------------------------------------------------- /config_files_gc/vinsmono/groundchallenge.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | 3 | #common parameters 4 | imu_topic: "/camera/imu" 5 | image_topic: "/camera/color/image_raw" 6 | output_path: "/home/car/Downloads/project/vinsmono_ws/src/VINS-Mono/traj/mono_initest/" 7 | 8 | #camera calibration 9 | model_type: PINHOLE 10 | camera_name: camera 11 | image_width: 640 12 | image_height: 480 13 | distortion_parameters: 14 | k1: 1.4865749308203452e-01 15 | k2: -4.6815685578576460e-01 16 | p1: 1.6205585303208318e-03 17 | p2: -8.9101576735577930e-03 18 | projection_parameters: 19 | fx: 6.2097277909374247e+02 20 | fy: 6.2212293397677581e+02 21 | cx: 3.1175896455154810e+02 22 | cy: 2.4718077836114819e+02 23 | 24 | 25 | # Extrinsic parameter between IMU and Camera. 26 | estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 27 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 28 | # 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. 29 | #If you choose 0 or 1, you should write down the following matrix. 30 | #Rotation from camera frame to imu frame, imu^R_cam 31 | # extrinsicRotation: !!opencv-matrix 32 | # rows: 3 33 | # cols: 3 34 | # dt: d 35 | # data: [ 0.99964659 ,-0.01617333 , 0.0210979, 36 | # 0.01635721, 0.99982947, -0.00857216, 37 | # -0.02095566, 0.00891423, 0.99974066] 38 | #idc cam to imu 39 | extrinsicRotation: !!opencv-matrix 40 | rows: 3 41 | cols: 3 42 | dt: d 43 | data: [ 0.99957087, -0.00192891 ,-0.02922921 , 44 | 0.00215313 ,0.99996848 , 0.00764156 , 45 | 0.02921355, -0.00770122 , 0.99954353 ] 46 | #Translation from camera frame to imu frame, imu^T_cam 47 | # extrinsicTranslation: !!opencv-matrix 48 | # rows: 3 49 | # cols: 1 50 | # dt: d 51 | # data: [-0.02323286, -0.01953139, 0.01917997] 52 | 53 | #idc 54 | extrinsicTranslation: !!opencv-matrix 55 | rows: 3 56 | cols: 1 57 | dt: d 58 | data: [-0.03575085, 0.0044559 , -0.03269785] 59 | 60 | #feature traker paprameters 61 | max_cnt: 200 # max feature number in feature tracking 62 | min_dist: 15 # min distance between two features 63 | freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image 64 | F_threshold: 1.0 # ransac threshold (pixel) 65 | show_track: 1 # publish tracking image as topic 66 | equalize: 0 # if image is too dark or light, trun on equalize to find enough features 67 | fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points 68 | 69 | #optimization parameters 70 | max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time 71 | max_num_iterations: 8 # max solver itrations, to guarantee real time 72 | keyframe_parallax: 10.0 # keyframe selection threshold (pixel) 73 | 74 | #imu parameters The more accurate parameters you provide, the better performance 75 | # acc_n: 2.67809809884e-04 # accelerometer measurement noise standard deviation. #0.2 0.04 76 | # gyr_n: 1.02960602817e-05 # gyroscope measurement noise standard deviation. #0.05 0.004 77 | # acc_w: 2.49999993684e-05 # accelerometer bias random work noise standard deviation. #0.002 78 | # gyr_w: 2.49999999369e-07 # gyroscope bias random work noise standard deviation. #4.0e-5 79 | # g_norm: 9.805 # gravity magnitude 80 | 81 | 82 | #first time full log 83 | # acc_n: 0.02 #2.9840535876578939e-02 # accelerometer measurement noise standard deviation. #0.2 84 | # gyr_n: 0.005 #4.8602774318549456e-03 # gyroscope measurement noise standard deviation. #0.05 85 | # acc_w: 0.01 #9.2450042830019173e-03 # accelerometer bias random work noise standard deviation. #0.02 86 | # gyr_w: 3.0e-5 #3.0172667291423203e-05 # gyroscope bias random work noise standard deviation. #4.0e-5 87 | # g_norm: 9.805 # gravity magnitude 88 | 89 | #m2dgr 90 | # gyr_n: 2.4710787075320089e-03 91 | # gyr_w: 1.7963145905200798e-05 92 | # acc_n: 2.6848761610624401e-02 93 | # acc_w: 8.5216274964016023e-04 94 | 95 | #test used this... 96 | # acc_n: 1.3414916806352631e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 97 | # gyr_n: 3.1272352813400007e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 98 | # acc_w: 2.3513079032355371e-04 # accelerometer bias random work noise standard deviation. #0.002 99 | # gyr_w: 4.7867403773944395e-05 100 | # g_norm: 9.805 # gravity magnitude 101 | 102 | #idc 103 | acc_n: 1.2374091609523514e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 104 | gyr_n: 3.0032654435730201e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 105 | acc_w: 1.9218003442176448e-04 # accelerometer bias random work noise standard deviation. #0.002 106 | gyr_w: 5.4692100664858005e-05 107 | g_norm: 9.805 # gravity magnitude 108 | 109 | #loop closure parameters 110 | loop_closure: 1 # start loop closure 111 | fast_relocalization: 1 # useful in real-time and large project 112 | load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' 113 | pose_graph_save_path: "/home/yinjie/Downloads/project/vinsmono_ws/src/VINS-Mono/posegraph/" # save and load path 114 | 115 | #unsynchronization parameters 116 | estimate_td: 1 # online estimate time offset between camera and imu 117 | td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 118 | 119 | #rolling shutter parameters 120 | rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera 121 | rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet). 122 | 123 | #visualization parameters 124 | save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0 125 | visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results 126 | visualize_camera_size: 0.4 # size of camera marker in RVIZ 127 | -------------------------------------------------------------------------------- /config_files_gc/vinsrgbd/groundchallenge_config.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | 3 | #common parameters 4 | imu_topic: "/camera/imu" 5 | image_topic: "/camera/color/image_raw" 6 | 7 | depth_topic: "/camera/aligned_depth_to_color/image_raw" 8 | output_path: "/home/car/Downloads/newproject/vinsrgbd_ws/src/VINS-RGBD/mytraj/rgbd_initest/" 9 | 10 | #pointcloud settings 11 | pcl_dist: 10 12 | u_boundary: 10 13 | d_boundary: 10 14 | l_boundary: 40 15 | r_boundary: 40 16 | pcl_min_dist: 0.3 17 | pcl_max_dist: 6 18 | resolution: 0.02 19 | 20 | #camera calibration 21 | 22 | model_type: PINHOLE 23 | camera_name: camera 24 | image_width: 640 25 | image_height: 480 26 | distortion_parameters: 27 | k1: 1.4865749308203452e-01 28 | k2: -4.6815685578576460e-01 29 | p1: 1.6205585303208318e-03 30 | p2: -8.9101576735577930e-03 31 | projection_parameters: 32 | fx: 6.2097277909374247e+02 33 | fy: 6.2212293397677581e+02 34 | cx: 3.1175896455154810e+02 35 | cy: 2.4718077836114819e+02 36 | 37 | 38 | 39 | # Extrinsic parameter between IMU and Camera. 40 | estimate_extrinsic: 1 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 41 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 42 | # 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. 43 | #If you choose 0 or 1, you should write down the following matrix. 44 | #Rotation from camera frame to imu frame, imu^R_cam 45 | extrinsicRotation: !!opencv-matrix 46 | rows: 3 47 | cols: 3 48 | dt: d 49 | data: [ 0.99957087, -0.00192891 ,-0.02922921 , 50 | 0.00215313 ,0.99996848 , 0.00764156 , 51 | 0.02921355, -0.00770122 , 0.99954353 ] 52 | #Translation from camera frame to imu frame, imu^T_cam 53 | extrinsicTranslation: !!opencv-matrix 54 | rows: 3 55 | cols: 1 56 | dt: d 57 | data: [-0.03575085, 0.0044559 , -0.03269785] 58 | 59 | #feature traker paprameters 60 | max_cnt: 200 # max feature number in feature tracking 61 | min_dist: 15 # min distance between two features 62 | freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image 63 | F_threshold: 1.0 # ransac threshold (pixel) 64 | show_track: 1 # publish tracking image as topic 65 | equalize: 0 # if image is too dark or light, trun on equalize to find enough features 66 | fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points 67 | 68 | #optimization parameters 69 | max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time 70 | max_num_iterations: 8 # max solver itrations, to guarantee real time 71 | keyframe_parallax: 10.0 # keyframe selection threshold (pixel) 72 | 73 | #imu parameters The more accurate parameters you provide, the better performance 74 | #for handheld, wheeld 75 | # acc_n: 0.1 # accelerometer measurement noise standard deviation. #0.2 76 | # gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05 77 | # acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02 78 | # gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5 79 | 80 | 81 | #first time test with full log 82 | # acc_n: 0.02 #2.9840535876578939e-02 # accelerometer measurement noise standard deviation. #0.2 83 | # gyr_n: 0.005 #4.8602774318549456e-03 # gyroscope measurement noise standard deviation. #0.05 84 | # acc_w: 0.01 #9.2450042830019173e-03 # accelerometer bias random work noise standard deviation. #0.02 85 | # gyr_w: 3.0e-5 #3.0172667291423203e-05 # gyroscope bias random work noise standard deviation. #4.0e-5 86 | # g_norm: 9.805 # gravity magnitude 87 | 88 | # #M2DGR 89 | # gyr_n: 2.4710787075320089e-03 90 | # gyr_w: 1.7963145905200798e-05 91 | # acc_n: 2.6848761610624401e-02 92 | # acc_w: 8.5216274964016023e-04 93 | # g_norm: 9.805 # gravity magnitude 94 | 95 | #idc 96 | acc_n: 1.2374091609523514e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 97 | gyr_n: 3.0032654435730201e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 98 | acc_w: 1.9218003442176448e-04 # accelerometer bias random work noise standard deviation. #0.002 99 | gyr_w: 5.4692100664858005e-05 100 | g_norm: 9.805 # gravity magnitude 101 | 102 | #for tracked applications 103 | #acc_n: 0.5 # accelerometer measurement noise standard deviation. #0.2 104 | #gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05 105 | #acc_w: 0.001 # accelerometer bias random work noise standard deviation. #0.02 106 | #gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5 107 | 108 | 109 | # g_norm: 9.805 # gravity magnitude 110 | 111 | #loop closure parameters 112 | loop_closure: 0 # start loop closure 113 | fast_relocalization: 1 # useful in real-time and large project 114 | load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' 115 | pose_graph_save_path: "/home/shanzy/output/pose_graph/" # save and load path 116 | 117 | #unsynchronization parameters 118 | estimate_td: 1 # online estimate time offset between camera and imu 119 | td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 120 | 121 | #rolling shutter parameters 122 | rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera 123 | rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet). 124 | 125 | #visualization parameters 126 | save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0 127 | visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results 128 | visualize_camera_size: 0.4 # size of camera marker in RVIZ 129 | -------------------------------------------------------------------------------- /config_files_gc/vinsrgbd/groundchallenge_depth_config.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | 3 | #camera calibration 4 | model_type: PINHOLE 5 | camera_name: camera 6 | image_width: 640 7 | image_height: 480 8 | distortion_parameters: 9 | k1: -0.0218853623318932 10 | k2: 0.0310360257949854 11 | p1: 0.000000 12 | p2: 0.0000000 13 | projection_parameters: 14 | fx: 1328.99172142228 15 | fy: 1304.45385325171 16 | cx: 332.823026423553 17 | cy: 245.451832455893 18 | -------------------------------------------------------------------------------- /config_files_gc/viwfusion/groundchallenge.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | 3 | #common parameters 4 | #support: 1 imu 1 cam; 1 imu 2 cam: 2 cam; 5 | imu: 1 6 | wheel: 1 7 | only_initial_with_wheel: 0 #只利用wheel进行初始化,不加入因子图 8 | plane: 0 9 | num_of_cam: 1 10 | 11 | imu_topic: "/camera/imu" 12 | wheel_topic: "/odom" #"/ridgeback_velocity_controller/odom", “/odometry/filtered” 13 | #TODO check the distortion 14 | image0_topic: "/camera/color/image_raw" 15 | image1_topic: "/camera/aligned_depth_to_color/image_raw" 16 | output_path: "/home/car/Downloads/newproject/viwfusion_ws/src/VIW-Fusion/output/viw_initest/" 17 | 18 | cam0_calib: "groundchallenge_cam.yaml" 19 | cam1_calib: "groundchallenge_cam.yaml" 20 | image_width: 640 21 | image_height: 480 22 | 23 | 24 | # Extrinsic parameter between IMU and Camera. 25 | estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 26 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 27 | # 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. 28 | #If you choose 0 or 1, you should write down the following matrix. 29 | 30 | extrinsic_type: 3 # 0 ALL 31 | # 1 Only translation 32 | # 2 Only Rotation 33 | # 3 no z 34 | # 4 no rotation and no z 35 | 36 | body_T_cam0: !!opencv-matrix 37 | rows: 4 38 | cols: 4 39 | dt: d 40 | data: [ 0.99957087 , 0.00215313 , 0.02921355 , 0.03668114, 41 | -0.00192891 ,0.99996848, -0.00770122 ,-0.00477653, 42 | -0.02922921 , 0.00764156 ,0.99954353 , 0.0316039, 43 | 0. , 0. , 0. , 1. ] 44 | 45 | 46 | body_T_cam1: !!opencv-matrix 47 | rows: 4 48 | cols: 4 49 | dt: d 50 | data: [ 0.99957087 , 0.00215313 , 0.02921355 , 0.03668114, 51 | -0.00192891 ,0.99996848, -0.00770122 ,-0.00477653, 52 | -0.02922921 , 0.00764156 ,0.99954353 , 0.0316039, 53 | 0. , 0. , 0. , 1. ] 54 | 55 | 56 | # Extrinsic parameter between IMU and Wheel. 57 | estimate_wheel_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 58 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 59 | # 2 Don't know anything about extrinsic parameters. You don't need to give R,T. We will try to calibrate it. Do some rotation movement at beginning. 60 | #If you choose 0 or 1, you should write down the following matrix. 61 | 62 | extrinsic_type_wheel: 0 # 0 ALL 63 | # 1 Only translation 64 | # 2 Only Rotation 65 | # 3 no z 66 | # 4 no rotation and no z 67 | 68 | #wheel to body 69 | body_T_wheel: !!opencv-matrix 70 | rows: 4 71 | cols: 4 72 | dt: d 73 | data: [0, -1, 0, 0, 74 | 0, 0, -1, 0, 75 | 1, 0, 0, 0, 76 | 0, 0, 0, 1] 77 | 78 | #plane noise 79 | #mono:0.01 stereo:0.005 80 | roll_n: 0.01 81 | #mono:0.01 stereo:0.005 82 | pitch_n: 0.01 83 | #mono:0.05 stereo:0.025 84 | zpw_n: 0.05 85 | 86 | 87 | #Multiple thread support 88 | multiple_thread: 1 89 | 90 | #feature traker paprameters 91 | max_cnt: 150 # max feature number in feature tracking 92 | min_dist: 30 # min distance between two features 93 | freq: 10 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image 94 | F_threshold: 1.0 # ransac threshold (pixel) 95 | show_track: 1 # publish tracking image as topic 96 | flow_back: 1 # perform forward and backward optical flow to improve feature tracking accuracy 97 | 98 | #optimization parameters 99 | max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time 100 | max_num_iterations: 8 # max solver itrations, to guarantee real time 101 | keyframe_parallax: 10.0 # keyframe selection threshold (pixel) 102 | 103 | #imu parameters The more accurate parameters you provide, the better performance 104 | acc_n: 1.2374091609523514e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 105 | gyr_n: 3.0032654435730201e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 106 | acc_w: 1.9218003442176448e-04 # accelerometer bias random work noise standard deviation. #0.002 107 | gyr_w: 5.4692100664858005e-05 # gyroscope bias random work noise standard deviation. #4.0e-5 108 | g_norm: 9.805 # gravity magnitude 109 | 110 | #wheel parameters 111 | # rad/s mono:0.004 stereo:0.002 112 | wheel_gyro_noise_sigma: 0.004 113 | # m/s mono:0.01 stereo:0.006 114 | wheel_velocity_noise_sigma: 0.01 115 | 116 | estimate_wheel_intrinsic: 0 117 | # 0 Have an accurate intrinsic parameters. We will trust the following sx, sy, sw, don't change it. 118 | # 1 Have an initial guess about intrinsic parameters. We will optimize around your initial guess. 119 | # 2 TODO Don't know anything about intrinsic parameters. You don't need to give sx, sy, sw. We will try to calibrate it. Do some rotation movement at beginning. 120 | #If you choose 0 or 1, you should write down the following sx, sy, sw. 121 | # wheel intrinsic 122 | sx: 1.0 123 | sy: 1.0 124 | sw: 1.0 125 | 126 | 127 | #unsynchronization parameters 128 | estimate_td: 0 # online estimate time offset between camera and imu 129 | td: 0.00 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 130 | #unsynchronization parameters 131 | estimate_td_wheel: 0 # online estimate time offset between camera and wheel 132 | td_wheel: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 133 | #loop closure parameters 134 | load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' 135 | pose_graph_save_path: "/home/td/slam/vins_fusion_ws/src/VINS-Fusion/output/pose_graph" # save and load path 136 | save_image: 0 # save image in pose graph for visualization prupose; you can close this function by setting 0 137 | -------------------------------------------------------------------------------- /config_files_gc/viwfusion/groundchallenge_cam.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | --- 3 | model_type: PINHOLE 4 | camera_name: camera 5 | image_width: 640 6 | image_height: 480 7 | distortion_parameters: 8 | k1: 1.4865749308203452e-01 9 | k2: -4.6815685578576460e-01 10 | p1: 1.6205585303208318e-03 11 | p2: -8.9101576735577930e-03 12 | projection_parameters: 13 | fx: 6.2097277909374247e+02 14 | fy: 6.2212293397677581e+02 15 | cx: 3.1175896455154810e+02 16 | cy: 2.4718077836114819e+02 17 | -------------------------------------------------------------------------------- /fig/corridor.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/sjtuyinjie/Ground-Challenge/215608762e849ba02458fbd6735bb20b1a8afade/fig/corridor.jpg 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