├── README.md ├── calibration.txt ├── config_files ├── gvins │ └── m2dgrp.yaml ├── vinsmono │ └── m2dgrp.yaml ├── vinsrgbd │ ├── m2dgrp_config.yaml │ └── m2dgrp_depth_config.yaml └── viwfusion │ ├── m2dgrp.yaml │ └── m2dgrp_cam.yaml ├── fig ├── car2.jpg ├── result.png ├── resultf.png └── sensor_detail.jpg └── gt ├── anomaly.txt ├── bridge1.txt ├── bridge2.txt ├── building1.txt ├── building2.txt ├── parking1.txt ├── parking2.txt ├── street1.txt ├── street2.txt ├── switch.txt └── tree.txt /README.md: -------------------------------------------------------------------------------- 1 | # M2DGR-plus: Extension and update of [M2DGR](https://github.com/SJTU-ViSYS/M2DGR), a novel Multi-modal and Multi-scenario SLAM Dataset for Ground Robots (ICRA2022 & ICRA2024) 2 | 3 |
4 | 5 | First Author: [**Jie Yin 殷杰**](https://sjtuyinjie.github.io/) 6 |   7 | 📝 [[Paper]](https://ieeexplore.ieee.org/document/10610070) / [[Arxiv]](https://arxiv.org/abs/2402.14308) 8 |   9 | 🎯 [[M2DGR Dataset]](https://github.com/SJTU-ViSYS/M2DGR) 10 |   11 | ⭐️ [[Presentation Video]](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7) 12 |   13 | 🔥[[News]](https://mp.weixin.qq.com/s/CfnfxHvn9pbYc4599_JoSg) 14 | 15 | [![Author](https://img.shields.io/badge/Author-Jie%20Yin-blue)](https://sjtuyinjie.github.io/) 16 | [![Paper](https://img.shields.io/badge/Paper-GroundFusion-yellow)](https://ieeexplore.ieee.org/document/10610070) 17 | [![Preprint](https://img.shields.io/badge/Preprint-Arxiv-purple)](https://arxiv.org/abs/2112.13659/) 18 | [![Dataset](https://img.shields.io/badge/Dataset-M2DGR+%2B-green)](https://github.com/SJTU-ViSYS/M2DGR-plus) 19 | [![License](https://img.shields.io/badge/License-GPLv3-cyan)]() 20 | [![Video](https://img.shields.io/badge/Video-red)](https://www.bilibili.com/video/BV1xx421m75k/?spm_id_from=333.337.search-card.all.click&vd_source=0804300aea4065df90adde5398ee74b7) 21 | [![News](https://img.shields.io/badge/News-orange)](https://mp.weixin.qq.com/s/CfnfxHvn9pbYc4599_JoSg) 22 | 23 |
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Figure 1. Acquisition Platform and Diverse Scenarios.

30 | 31 | 32 | 33 | 34 | ## News & Updates 35 | - **🔥`2024/10/11`**: Introducing **[M2DGR-benchmark](https://github.com/sjtuyinjie/M2DGR-Benchmark)**, benchmarking newest SOTA LiDAR-visual SLAM alrogithms on both M2DGR and M2DGR-plus! 36 | 37 | - **`2024/07/15`**: Introducing a list of LiDAR-Visual SLAM systems at [awesome-LiDAR-Visual-SLAM](https://github.com/sjtuyinjie/awesome-LiDAR-Visual-SLAM), wheel-based SLAM systems at [awesome-wheel-slam](https://github.com/sjtuyinjie/awesome-wheel-slam), and Isaac Sim resources at [awesome-isaac-sim](https://github.com/sjtuyinjie/awesome-isaac-sim) (keep updating) 38 | 39 | 40 | ### This dataset is based on [M2DGR](https://github.com/SJTU-ViSYS/M2DGR). And the algorithm code is [Ground-Fusion](https://github.com/SJTU-ViSYS/Ground-Fusion). The preprint version of this paper is [arxiv](http://arxiv.org/abs/2402.14308). 41 | 42 | ## 1.LICENSE 43 | This work is licensed under GPL-3.0 license. International License and is provided for academic purpose. If you are interested in our project for commercial purposes, please contact us on robot_yinjie@outlook.com for further communication. 44 | 45 | If you use this work in an academic work, please cite: 46 | ~~~ 47 | @article{yin2021m2dgr, 48 | title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots}, 49 | author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping}, 50 | journal={IEEE Robotics and Automation Letters}, 51 | volume={7}, 52 | number={2}, 53 | pages={2266--2273}, 54 | year={2021}, 55 | publisher={IEEE} 56 | } 57 | 58 | @INPROCEEDINGS{yin2024ground, 59 | author={Yin, Jie and Li, Ang and Xi, Wei and Yu, Wenxian and Zou, Danping}, 60 | booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)}, 61 | title={Ground-Fusion: A Low-cost Ground SLAM System Robust to Corner Cases}, 62 | year={2024}, 63 | volume={}, 64 | number={}, 65 | pages={8603-8609}, 66 | keywords={Location awareness;Visualization;Simultaneous localization and mapping;Accuracy;Wheels;Sensor fusion;Land vehicles}, 67 | doi={10.1109/ICRA57147.2024.10610070}} 68 | 69 | ~~~ 70 | 71 | ## 2.SENSOR SETUP 72 | 73 | 74 | 75 | The calibration results are [here](https://github.com/SJTU-ViSYS/M2DGR-plus/blob/main/calibration.txt). 76 | All the sensors and track devices and their most important parameters are listed as below: 77 | 78 | * **LIDAR** Robosense 16, 360 Horizontal Field of View (FOV),-30 to +10 vertical FOV,5Hz,Max Range 200 m,Range Resolution 3 cm, Horizontal Angular Resolution 0.2°. 79 | * **GNSS** Ublox F9p, GPS/BeiDou/Glonass/Galileo, 1Hz 80 | * **V-I Sensor**,Realsense d435i,RGB/Depth 640*480,69H-FOV,42.5V-FOV,15Hz;IMU 6-axix, 200Hz 81 | * **IMU**,wheeltec,9-axis,100Hz; 82 | * **GNSS-IMU** Xsens Mti 680G. GNSS-RTK,localization precision 2cm,100Hz;IMU 9-axis,100 Hz; 83 | * **Motion-capture System** Vicon Vero 2.2, localization accuracy 1mm, 50 Hz; 84 | 85 | The rostopics of our rosbag sequences are listed as follows: 86 | 87 | * 3D LIDAR: `/rslidar_points` 88 | 89 | * 2D LIDAR: `/scan` 90 | 91 | * Odom: `/odom` 92 | 93 | * GNSS Ublox F9p: 94 | `/ublox_driver/ephem `, 95 | 96 | `/ublox_driver/glo_ephem `, 97 | 98 | `/ublox_driver/range_meas `, 99 | 100 | `/ublox_driver/receiver_lla `, 101 | 102 | `/ublox_driver/receiver_pvt ` 103 | 104 | 105 | * V-I Sensor: 106 | `/camera/color/image_raw`, 107 | `/camera/imu` 108 | 109 | * IMU: `/imu ` 110 | 111 | 112 | ## 3.DATASET SEQUENCES 113 | 114 | 115 | > [!TIP] 116 | > We are delighted to see that many users enjoy our M2DGR-plus 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.** 117 | 118 | Sequence Name|Collection Date|Total Size|Duration|Features|Rosbag 119 | --|:--|:--:|--:|--:|--: 120 | Anomaly|2023-8|1.5g|57s|wheel anomaly|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ef8corMuVwhJsWSpp-FXkREBrTduGBO8nifC9VEb5twHVg?e=CyEeMy) 121 | Switch|2023-8|9.5g|292s|indoor-outdoor switch|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/ESRoBZtYjrtAkOzZZZxjtLIBowQqF3G9Vz-jiaUCCy6E_A?e=RtZkwL) 122 | Tree|2023-8|3.7g|160s|Dense tree leave cover|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EV9aZQbxo7pChOxZEWqdP0IBDpySkhtOXNIRKP3ijDK62Q?e=fW0afm) 123 | Bridge_01|2022-11|2.4g|75s|Bridge, Zigzag|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EftDI1uQ_M1Hp4LZVof4sHgB4_IF2C9HBsWYZKAK2mr4EA?e=dydvKz) 124 | Bridge_02|2022-11|16.0g|501s|Bridge, Long-term,Straight line|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUrTvD2zK2hNimekHiJS5rABME45O5s7ksSAJpd3ipD-BA?e=7aicGk) 125 | Street_01|2022-11|1.7g|58s|Street, Straight line|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/Ebap2epwtTtHhWtp0AO_nnYB7S7zDZkkW-zTpYVmrHfOEA?e=JvDij7) 126 | Street_02|2022-11|3.9g|126s|Bridge, Sharp turn|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EdqjNDDhVkJNhwA9DKlPnTsBIXh0xCGITpvQ1b4bG__k0A?e=chWjV8) 127 | Parking_01|2022-11|3.3g|105s|Parking lot, Side moving|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EaCXXT2SAP9AmmqR1LUYwu4By3z5P3jhdeROv8EPdp9C0A?e=fQqJq5) 128 | Parking_02|2022-11|5.4g|149s|Parking lot, Rectangle loop|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EX7UjX535NZBkaXSIX63Pg4BMDGXfIfkjS7JvL-0lUA8mQ?e=lAMTTu) 129 | Building_01|2022-11|3.7g|120s|Building, Far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EUqNPeUal1JDnSd9ZbYKo5EBoaQKrna5m23B7LxzAB-mtQ?e=QtWUal) 130 | Building_02|2022-11|3.4g|110s|Building, Far features|[Rosbag](https://sjtueducn-my.sharepoint.com/:u:/g/personal/594666_sjtu_edu_cn/EQcO7OhqhYlCv1tAQJeD6EkBi50Ot3OPajKtZmZJlJFyUw?e=NBS9rs) 131 | 132 | 133 | 134 | 135 | 136 | ## 4. EXPERIMENTAL RESULTS 137 | ### We test methods with diverse senser settings to validate our benchmark dataset. Results shown that our dataset is a valid and effective testfield for localization methods. 138 | And in some cases, our Ground-Fusion achieves comparable performance to Lidar SLAM! 139 |
140 | 141 | 142 |
143 |

Figure 2. The ATE RMSE (m) result on some sequences.

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149 |

Figure 3. The visualized trajectory.

150 | 151 | 152 | ## 5. Configuration Files 153 | We provide configuration files for several cutting-edge baseline methods, including [VINS-RGBD](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/vinsrgbd),[TartanVO](https://github.com/SJTU-ViSYS/Ground-Challenge/tree/main/config_files_gc/tartanvo),[VINS-Mono](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/vinsmono) and [VIW-Fusion](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/viwfusion) and 154 | [GVINS](https://github.com/SJTU-ViSYS/M2DGR-plus/tree/main/config_files/gvins). 155 | 156 | 157 | ## Star History 158 | 159 | [![Star History Chart](https://api.star-history.com/svg?repos=SJTU-ViSYS/M2DGR-plus&type=Timeline)](https://star-history.com/#Ashutosh00710/github-readme-activity-graph&Timeline) 160 | 161 | -------------------------------------------------------------------------------- /calibration.txt: -------------------------------------------------------------------------------- 1 | #A2B: Convert from coordinate system B to coordinate system A 2 | rsimu2wheel: 3 | rows: 4 4 | cols: 4 5 | dt: d 6 | data: [0.352551，-0.935764，-0.00734672，0.0497956， 7 | 0.0145238，0.0133214，-0.999806 ，1.06332， 8 | 0.93568，0.352375， 0.0182873，-0.037465， 9 | 0，0，0，1] 10 | 11 | imu2wheel: 12 | rows: 4 13 | cols: 4 14 | dt: d 15 | data: [0.9357，0.3524，0.0183，0.1225， 16 | 0.3526，-0.9358，-0.0073，0.0498， 17 | 0.0145，0.0133，-0.9998，1.0633， 18 | 0，0，0，1] 19 | 20 | lidar2wheel: 21 | rows: 4 22 | cols: 4 23 | dt: d 24 | data: [0.9357，0.3524，0.0183，-0.0075， 25 | -0.3526，0.9358，0.0073，-0.0498， 26 | -0.0145，-0.0133，0.9998，-2.0933， 27 | 0，0，0，1] 28 | 29 | rsimu2camera: 30 | rows: 4 31 | cols: 4 32 | dt: d 33 | data: [1 ，0，0，0.0， 34 | 0，1，0 ，0.0， 35 | 0 ，0，1，0.0， 36 | 0 ，0 ，0，1.0，] 37 | 38 | lidar2imu: 39 | rows: 4 40 | cols: 4 41 | dt: d 42 | data: [1, 0, 0, -0.13, 43 | 0, -1, 0, 0, 44 | 0, 0, -1, -1.03, 45 | 0, 0, 0, 1] 46 | 47 | camera2imu: 48 | rows: 4 49 | cols: 4 50 | dt: d 51 | data: [0, 1, 0, 0, 52 | 0, 0, 1, 0, 53 | 1, 0, 0, -0.16, 54 | 0, 0, 0, 1] 55 | 56 | lidar2camera: 57 | rows: 4 58 | cols: 4 59 | dt: d 60 | data: [0, 0, 1, 0.03, 61 | -1, 0, 0, 0.0, 62 | 0, -1, 0, -1.03, 63 | 0, 0, 0, 1] 64 | 65 | 66 | #imu parameters 67 | acc_n: 1.2374091609523514e-02 68 | gyr_n: 3.0032654435730201e-03 69 | acc_w: 1.9218003442176448e-04 70 | gyr_w: 5.4692100664858005e-05 71 | g_norm: 9.805 72 | 73 | # rsimu Settings 74 | rsimuAccNoise: 0.04 75 | rsimuGyrNoise: 0.004 76 | rsimuAccBiasN: 0.002 77 | rsimuGyrBiasN: 4.0e-5 78 | rsimuGravity: 9.80511 79 | 80 | #lidar Settings 81 | N_SCAN: 16 82 | Horizon_SCAN: 1800 83 | 84 | # camera config 85 | model_type: PINHOLE 86 | camera_name: camera 87 | image_width: 640 88 | image_height: 480 89 | distortion_parameters: 90 | k1: 0.0 91 | k2: 0.0 92 | p1: 0.0 93 | p2: 0.0 94 | projection_parameters: 95 | fx: 603.95556640625 96 | fy: 603.1257934570312 97 | cx: 324.0858154296875 98 | cy: 232.72303771972656 99 | -------------------------------------------------------------------------------- /config_files/gvins/m2dgrp.yaml: -------------------------------------------------------------------------------- 1 | %YAML:1.0 2 | 3 | #common parameters 4 | imu_topic: "/camera/imu" 5 | image_topic: "/camera/color/image_raw" 6 | output_dir: "//home/car/Downloads/project/gvins_ws/src/GVINS/traj/m2dgrp/" 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: 0.0 15 | k2: 0.0 16 | p1: 0.0 17 | p2: 0.0 18 | projection_parameters: 19 | fx: 603.95556640625 20 | fy: 603.1257934570312 21 | cx: 324.0858154296875 22 | cy: 232.72303771972656 23 | 24 | 25 | gnss_enable: 0 26 | gnss_meas_topic: "/ublox_driver/range_meas" # GNSS raw measurement topic 27 | gnss_ephem_topic: "/ublox_driver/ephem" # GPS, Galileo, BeiDou ephemeris 28 | gnss_glo_ephem_topic: "/ublox_driver/glo_ephem" # GLONASS ephemeris 29 | gnss_iono_params_topic: "/ublox_driver/iono_params" # GNSS broadcast ionospheric parameters 30 | gnss_tp_info_topic: "/ublox_driver/time_pulse_info" # PPS time info 31 | gnss_elevation_thres: 30 # satellite elevation threshold (degree) 32 | gnss_psr_std_thres: 2.0 # pseudo-range std threshold 33 | gnss_dopp_std_thres: 2.0 # doppler std threshold 34 | gnss_track_num_thres: 5 # number of satellite tracking epochs before entering estimator 35 | gnss_ddt_sigma: 0.1 36 | 37 | gnss_local_online_sync: 0 # if perform online synchronization betwen GNSS and local time 38 | local_trigger_info_topic: "/external_trigger" # external trigger info of the local sensor, if `gnss_local_online_sync` is 1 39 | gnss_local_time_diff: 18.0 # difference between GNSS and local time (s), if `gnss_local_online_sync` is 0 40 | 41 | gnss_iono_default_parameters: !!opencv-matrix 42 | rows: 1 43 | cols: 8 44 | dt: d 45 | data: [0.1118E-07, 0.2235E-07, -0.4172E-06, 0.6557E-06, 46 | 0.1249E+06, -0.4424E+06, 0.1507E+07, -0.2621E+06] 47 | 48 | # Extrinsic parameter between IMU and Camera. 49 | estimate_extrinsic: 0 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 50 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 51 | # 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. 52 | #If you choose 0 or 1, you should write down the following matrix. 53 | #Rotation from camera frame to imu frame, imu^R_cam 54 | extrinsicRotation: !!opencv-matrix 55 | rows: 3 56 | cols: 3 57 | dt: d 58 | data: [1.0, -0.00, 0.00, 59 | 0.00, 1.0, 0.0, 60 | -0.00, 0.00, 1.0] 61 | #Translation from camera frame to imu frame, imu^T_cam 62 | extrinsicTranslation: !!opencv-matrix 63 | rows: 3 64 | cols: 1 65 | dt: d 66 | data: [-0.0, -0.00, -0.0] 67 | 68 | #feature traker paprameters 69 | max_cnt: 150 # max feature number in feature tracking 70 | min_dist: 30 # min distance between two features 71 | freq: 0 # frequence (Hz) of publish tracking result. At least 10Hz for good estimation. If set 0, the frequence will be same as raw image 72 | F_threshold: 1.0 # ransac threshold (pixel) 73 | show_track: 1 # publish tracking image as topic 74 | equalize: 1 # if image is too dark or light, trun on equalize to find enough features 75 | fisheye: 0 # if using fisheye, trun on it. A circle mask will be loaded to remove edge noisy points 76 | 77 | #optimization parameters 78 | max_solver_time: 0.04 # max solver itration time (ms), to guarantee real time 79 | max_num_iterations: 8 # max solver itrations, to guarantee real time 80 | keyframe_parallax: 10.0 # keyframe selection threshold (pixel) 81 | 82 | #imu parameters The more accurate parameters you provide, the better performance 83 | acc_n: 1.2374091609523514e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 84 | gyr_n: 3.0032654435730201e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 85 | acc_w: 1.9218003442176448e-04 # accelerometer bias random work noise standard deviation. #0.002 86 | gyr_w: 5.4692100664858005e-05 87 | g_norm: 9.805 # gravity magnitude 88 | 89 | #unsynchronization parameters 90 | estimate_td: 0 # online estimate time offset between camera and imu 91 | td: 0.0 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 92 | -------------------------------------------------------------------------------- /config_files/vinsmono/m2dgrp.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/m2dgrp/" 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: 0.0 15 | k2: 0.0 16 | p1: 0.0 17 | p2: 0.0 18 | projection_parameters: 19 | fx: 603.95556640625 20 | fy: 603.1257934570312 21 | cx: 324.0858154296875 22 | cy: 232.72303771972656 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 # gyroscope bias random work noise standard deviation. #4.0e-5 107 | g_norm: 9.805 # gravity magnitude 108 | 109 | 110 | #loop closure parameters 111 | loop_closure: 1 # start loop closure 112 | fast_relocalization: 1 # useful in real-time and large project 113 | load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' 114 | pose_graph_save_path: "/home/yinjie/Downloads/project/vinsmono_ws/src/VINS-Mono/posegraph/" # save and load path 115 | 116 | #unsynchronization parameters 117 | estimate_td: 1 # online estimate time offset between camera and imu 118 | td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 119 | 120 | #rolling shutter parameters 121 | rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera 122 | rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet). 123 | 124 | #visualization parameters 125 | save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0 126 | visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results 127 | visualize_camera_size: 0.4 # size of camera marker in RVIZ 128 | -------------------------------------------------------------------------------- /config_files/vinsrgbd/m2dgrp_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/m2dgrp/" 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: 0.0 28 | k2: 0.0 29 | p1: 0.0 30 | p2: 0.0 31 | projection_parameters: 32 | fx: 603.95556640625 33 | fy: 603.1257934570312 34 | cx: 324.0858154296875 35 | cy: 232.72303771972656 36 | 37 | 38 | 39 | 40 | # Extrinsic parameter between IMU and Camera. 41 | estimate_extrinsic: 1 # 0 Have an accurate extrinsic parameters. We will trust the following imu^R_cam, imu^T_cam, don't change it. 42 | # 1 Have an initial guess about extrinsic parameters. We will optimize around your initial guess. 43 | # 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. 44 | #If you choose 0 or 1, you should write down the following matrix. 45 | #Rotation from camera frame to imu frame, imu^R_cam 46 | extrinsicRotation: !!opencv-matrix 47 | rows: 3 48 | cols: 3 49 | dt: d 50 | data: [ 0.99957087, -0.00192891 ,-0.02922921 , 51 | 0.00215313 ,0.99996848 , 0.00764156 , 52 | 0.02921355, -0.00770122 , 0.99954353 ] 53 | #Translation from camera frame to imu frame, imu^T_cam 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 | #for handheld, wheeld 76 | # acc_n: 0.1 # accelerometer measurement noise standard deviation. #0.2 77 | # gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05 78 | # acc_w: 0.0002 # accelerometer bias random work noise standard deviation. #0.02 79 | # gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5 80 | 81 | 82 | #first time test with 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 | # g_norm: 9.805 # gravity magnitude 95 | 96 | #idc 97 | acc_n: 1.2374091609523514e-02 # accelerometer measurement noise standard deviation. #0.2 0.04 98 | gyr_n: 3.0032654435730201e-03 # gyroscope measurement noise standard deviation. #0.05 0.004 99 | acc_w: 1.9218003442176448e-04 # accelerometer bias random work noise standard deviation. #0.002 100 | gyr_w: 5.4692100664858005e-05 101 | g_norm: 9.805 # gravity magnitude 102 | 103 | #for tracked applications 104 | #acc_n: 0.5 # accelerometer measurement noise standard deviation. #0.2 105 | #gyr_n: 0.01 # gyroscope measurement noise standard deviation. #0.05 106 | #acc_w: 0.001 # accelerometer bias random work noise standard deviation. #0.02 107 | #gyr_w: 2.0e-5 # gyroscope bias random work noise standard deviation. #4.0e-5 108 | 109 | 110 | # g_norm: 9.805 # gravity magnitude 111 | 112 | #loop closure parameters 113 | loop_closure: 0 # start loop closure 114 | fast_relocalization: 1 # useful in real-time and large project 115 | load_previous_pose_graph: 0 # load and reuse previous pose graph; load from 'pose_graph_save_path' 116 | pose_graph_save_path: "/home/shanzy/output/pose_graph/" # save and load path 117 | 118 | #unsynchronization parameters 119 | estimate_td: 1 # online estimate time offset between camera and imu 120 | td: 0.000 # initial value of time offset. unit: s. readed image clock + td = real image clock (IMU clock) 121 | 122 | #rolling shutter parameters 123 | rolling_shutter: 0 # 0: global shutter camera, 1: rolling shutter camera 124 | rolling_shutter_tr: 0.033 # unit: s. rolling shutter read out time per frame (from data sheet). 125 | 126 | #visualization parameters 127 | save_image: 1 # save image in pose graph for visualization prupose; you can close this function by setting 0 128 | visualize_imu_forward: 0 # output imu forward propogation to achieve low latency and high frequence results 129 | visualize_camera_size: 0.4 # size of camera marker in RVIZ 130 | -------------------------------------------------------------------------------- /config_files/vinsrgbd/m2dgrp_depth_config.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: 0.0 9 | k2: 0.0 10 | p1: 0.0 11 | p2: 0.0 12 | projection_parameters: 13 | fx: 603.95556640625 14 | fy: 603.1257934570312 15 | cx: 324.0858154296875 16 | cy: 232.72303771972656 17 | -------------------------------------------------------------------------------- /config_files/viwfusion/m2dgrp.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/m2dgrp/" 17 | 18 | cam0_calib: "m2dgrp_cam.yaml" 19 | cam1_calib: "m2dgrp_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/viwfusion/m2dgrp_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: 0.0 9 | k2: 0.0 10 | p1: 0.0 11 | p2: 0.0 12 | projection_parameters: 13 | fx: 603.95556640625 14 | fy: 603.1257934570312 15 | cx: 324.0858154296875 16 | cy: 232.72303771972656 17 | 18 | -------------------------------------------------------------------------------- /fig/car2.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SJTU-ViSYS/M2DGR-plus/e03b28a60d7569a1759a133acd29536f9ba69cff/fig/car2.jpg -------------------------------------------------------------------------------- /fig/result.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SJTU-ViSYS/M2DGR-plus/e03b28a60d7569a1759a133acd29536f9ba69cff/fig/result.png -------------------------------------------------------------------------------- /fig/resultf.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SJTU-ViSYS/M2DGR-plus/e03b28a60d7569a1759a133acd29536f9ba69cff/fig/resultf.png -------------------------------------------------------------------------------- /fig/sensor_detail.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/SJTU-ViSYS/M2DGR-plus/e03b28a60d7569a1759a133acd29536f9ba69cff/fig/sensor_detail.jpg -------------------------------------------------------------------------------- /gt/anomaly.txt: -------------------------------------------------------------------------------- 1 | 1691991959.119890213 0.0 0.0 0.0 1.0 0.0 0.0 0.0 2 | 1691991959.318526983 -0.00242897000266 0.00360622967669 -0.00253457658842 0.999999530904 0.000801243413919 -0.000490426511889 -0.000235972835666 3 | 1691991959.518533468 -0.00410698542811 0.00476819060359 0.00139462144555 0.999997385877 0.00209959943379 0.000749353341908 -0.000508322241224 4 | 1691991959.717109919 -0.00623102370853 0.00653392764793 0.000656850350117 0.999995999834 0.00255970628728 0.000939510102085 -0.00075202432095 5 | 1691991959.915848970 -0.00953647228044 0.00809290902559 0.00637595924301 0.999991306301 0.00396276824983 0.000981809201086 -0.000848434168383 6 | 1691991960.115822792 -0.0116841103818 0.00788971113268 0.00265413080478 0.999990459911 0.00414088931816 0.001188367279 -0.000721737474332 7 | 1691991960.314418316 -0.00952595363257 0.00744698833045 -0.000923110905331 0.99999189693 0.0038701188651 0.00103590299554 -0.000393902649303 8 | 1691991960.514393091 -0.0118550451014 0.00779869630828 -0.00164218843398 0.999988031058 0.00468592801031 0.00138702610905 -0.00023659493702 9 | 1691991960.713068962 -0.0146273392682 0.00758374143103 -0.00177047849127 0.999991696042 0.00385347235291 0.00131535996268 0.000168597944111 10 | 1691991960.911706209 -0.0112780727427 0.00574334349847 0.000275475574483 0.999987838991 0.00455413765604 0.00187713918515 -0.000240934979413 11 | 1691991961.111802578 0.00301151726243 0.000499914883616 -0.00111002851746 0.999977573029 0.00403560832472 0.00125819790781 -0.0051946359714 12 | 1691991961.310394049 -0.0013911399318 0.00602698671949 -0.00193822755221 0.999860263756 0.00245042396835 0.000850244711994 -0.0165144018611 13 | 1691991961.510337830 -0.00671093206864 0.000339074685608 -0.00398253722556 0.999789892072 0.00272404177987 0.001595194165 -0.0202535592774 14 | 1691991961.708997011 -0.0195891277997 0.0046997457094 0.0012576054906 0.999625133201 0.00409129943861 0.00384519703542 -0.0267968058088 15 | 1691991961.909010172 -0.0615080697235 0.00851685091764 0.000874659475353 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