├── LICENSE
├── README.md
└── docs
└── images
├── fig_all_traj_google_earth.png
├── fig_data_sample.png
├── fig_data_structure.png
├── fig_intro.png
├── fig_qualitative_6-min.png
├── gif_projection1.gif
├── gif_projection2.gif
└── ntu_logo.webp
/LICENSE:
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3 | Copyright (c) 2023 junzhang2016
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/README.md:
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1 |
2 | 
3 |
4 | # NTU4DRadLM：NTU 4D Radar-centric Multi-Modal Dataset for Localization and Mapping
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6 | 2023/10/22 update, Download Link:
7 | [NTU4DRadLM](https://rebrand.ly/ntu4dradlm)
8 |
9 | To test the dataset, our 4DRadarSLAM system is open-sourced at:
10 | [4DRadarSLAM](https://github.com/zhuge2333/4DRadarSLAM)
11 |
12 | Paper on arxiv: [Paper](https://arxiv.org/abs/2309.00962) (ITSC 2023 accepted)
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14 | This repository provides a description and acquisition of NTU4DRadLM.
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20 | Example data from our dataset with 4D radar, 3D LiDAR, thermal camera and visual camera. Top: radar (blue) and lidar (red) point cloud. Bottom: projection result from LiDAR onto rgb, LiDAR onto thermal, radar onto rgb, and radar onto thermal.
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30 | Example data from our dataset with 4D radar, 3D LiDAR, thermal camera and visual camera.
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42 |
43 | ## Introduction
44 | NTU4DRadLM is a novel 4D radar dataset specifically proposed for research on **robust SLAM**, based on 4D radar, thermal camera, and IMU. Totally, the dataset is around **17.6km**, **85mins**, **50GB**.
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46 |
47 |
48 | ### Motivation
49 | Simultaneous Localization and Mapping (SLAM) is moving towards a robust perception age. However, LiDAR- and visual- SLAM may easily fail in adverse conditions (rain, snow, smoke and fog, etc.). In comparison, SLAM based on 4D Radar, thermal camera and IMU can work robustly. But only a few literature can be found. A major reason is the lack of related datasets, which seriously hinders the research. Even though
50 | some datasets are proposed based on 4D radar in past four years, they are mainly designed for object detection, rather than SLAM. Furthermore, they normally do not include thermal camera. Therefore, in this paper, NTU4DRadLM is presented to meet this requirement.
51 |
52 | ### Characteristics:
53 |
54 | 1. It is the only dataset that simultaneously includes all 6 sensors: 4D radar, thermal camera, IMU, 3D LiDAR, visual camera and RTK GPS.
55 | 2. Specifically designed for SLAM tasks, which provides fine-tuned ground truth odometry and intentionally formulated loop closures.
56 | 3. Considered both low-speed robot platform and fast-speed unmanned vehicle platform.
57 | 4. Covered structured, unstructured and semi-structured environments.
58 | 5. Considered both middle- and large- scale outdoor environments, i.e., the 6 trajectories range from 246m to 6.95km.
59 | 6. Comprehensively evaluated three types of SLAM algorithms:
60 | - pure 4D radar based
61 | - 4D radar + IMU fused
62 | - 4D radar + thermal camera fused
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Data acquisition platform.
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80 | The six trajectories, cp: NTU carpark P (246m), garden: NTU Yunnan Garden (339m), nyl: Nanyang Link (1017m), loop1: big loop of NTU campus boundary (6.95km), loop2: loop of the south half of NTU campus (4.79km), loop3: loop of north half of NTU campus (4.23km).
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88 | The data samples.
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96 | Comparison of three types of radar SLAM on the 6 trajectories.
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