💎 Projcet Leader: Jie Yin 殷杰 🌐 [Website] 📝 [Paper] ➡️ [Algorithm Code] ⭐️ [Pre Video] 🔥 [News]
Our goal is to benchmark "all" cutting-edge SLAM!
This repository contains the official implementation of our IROS 2025 paper:
"Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and a Resilient Framework"
In this work, we propose a complete solution for robust SLAM on ground robots operating under degraded conditions. Our key contributions are:
- Ground-Fusion++ (Link): A resilient and modular SLAM framework integrating heterogeneous sensors for robust localization and high-quality mapping.
- M3DGR Benchmark(Link): A challenging multi-sensor, multi-scenario SLAM benchmark dataset with systematiclly induced degradation.
- Comprehensive Evaluation(Link): A comprehensive evaluation of over 40 cutting-edge SLAM methods on M3DGR.
2025.06.16: Our paper has been accepted to IROS 2025! We will release all datasets and code soon. Please stay tuned!
- Release camera-ready version of IROS2025 paper.[paper]
- Release 40 SLAM codes adapted for M3DGR dataset.[codes]
- Release Ground-Fusion++ code, with examples on M3DGR on M2DGR-plus. [code]
- Release most sequences in the paper included with GT and calibration files to make sure all results can be reproduced.[data]
- Release long-term sequences upon our journal paper acception.
🔍 For those interested in accessing the unreleased M3DGR sequences in advance, we recommend first thoroughly evaluating your methods on the already released sequences. After that, feel free to contact us at zhangjunjie587@gmail.com to request early access for research purposes.
The M3DGR project is an open and collaborative effort. We encourage you to adapt and evaluate your SLAM or localization algorithms on top of the M3DGR dataset! Our goal is to build an open and dynamic community, where researchers can not only use the dataset, but also contribute back by:
- Integrating your algorithms as baseline methods, which can enable fair comparison and promote your algorithm.
- Sharing configuration files, evaluation results, and insights.
Let’s make M3DGR a growing hub for robust, reproducible SLAM research! You can
- Submit a Pull Request to contribute new algorithms, configuration files, or improvements via Github Pull Request to post your adapted codes [here]
- Report bugs or request features via GitHub Issues.
- Join discussions or ask questions on GitHub Discussions.
Physical drawings and schematics of the ground robot. (a) Side view of the robot. (b) Sensor arrangement on the top layer. (c) Sensor arrangement on the middle and bottom layers. All dimensions are provided in centimeters.
Figure 1. The directions of the sensors are marked in different colors,red for X,green for Y and blue for Z.
All the sensors and track devices and their most important parameters are listed as below:
Sensor Specs:
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LiDAR1 Livox Avia, Non-repetitive, 70.4° Horizontal Field of View (FOV), 77.2° vertical FOV, 10HZ, Max Range 450m, Range Precision 2cm, Angular Precision 0.05º, IMU 6-axis 200HZ.
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LiDAR2 Livox MID-360, Non-repetitive, 360° Horizontal Field of View (FOV), -7° to +52° vertical FOV, 10Hz, Max Range 40 m, Range Resolution 3 cm, Angular Resolution 0.15°, IMU 6-axis, 200HZ.
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V-I Sensor Realsense d435i, RGB/Depth 640*480, 69°H-FOV, 42.5°V-FOV,15Hz; IMU 6-axis, 200Hz.
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Omnidirectional Camera Insta360 X4, RGB 2880*1440, 360°H-FOV, 360°V-FOV, 15HZ.
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Wheel Odometer WHEELTEC, 2D, 20HZ.
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GNSS Receiver CUAV C-RTK9Ps, BDS/GPS/GLONASS/Galileo, 10HZ.
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RTK Receiver CUAV C-RTK2HP, localization accuracy 0.8cm(H)/1.5cm(V), 15HZ.
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Motion-capture System OptiTrack, localization accuracy 1mm, 360HZ.
The rostopics of our rosbag sequences are listed as follows:
ROS topics:
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LiDAR1:
/livox/avia/lidar -
LiDAR2:
/livox/mid360/lidar -
Wheel Odometer：
/odom -
RGB Camera:
/camera/color/image_raw/compressed -
Omnidirectional Camera:
/cv_camera/image_raw/compressed -
Depth Camera:
/camera/aligned_depth_to_color/image_raw/compressedDepth -
GNSS:
/ublox_driver/ephem,
/ublox_driver/glo_ephem,
/ublox_driver/iono_params,
/ublox_driver/range_meas,
/ublox_driver/receiver_lla,
/ublox_driver/receiver_pvt,
/ublox_driver/time_pulse_info -
IMU:
/camera/imu,
/livox/avia/imu,
/livox/mid360/imu
Figure 2. All trajectories are mapped in different colors.
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An overview of M3DGR is given in the table below:
| Scenario | Visual Challenge | LiDAR Degeneracy | Wheel Slippage | GNSS Denial | Standard | TOTAL | |||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dark | VI¹ | Dynamic | Occlusion | Corridor | Elevator | WF² | ST³ | Grass | RR⁴ | ||||
| Number | 5 | 4 | 3 | 4 | 2 | 1 | 2 | 2 | 2 | 1 | 2 | 4 | 32 |
| Dist/m | 1653.31 | 1055.58 | 355.97 | 1091.24 | 545.64 | 470.64 | 101.55 | 170.88 | 318.91 | 457.35 | 1162.39 | 4485.49 | 11868.95 |
| Duration/s | 2274 | 1458 | 609 | 1224 | 696 | 699 | 171 | 238 | 459 | 533 | 1359 | 5101 | 14821 |
| Size/GB | 27.0 | 20.0 | 7.1 | 12.3 | 11.9 | 11.2 | 3.3 | 2.9 | 9.7 | 10.4 | 23.2 | 86.0 | 225.0 |
| GroundTruth | RTK/Mocap | RTK/Mocap | RTK/Mocap | RTK/Mocap | ArUco | ArUco | Mocap | Mocap | RTK | RTK | ArUco | RTK | ---- |
Figure 3. Outdoor01 Sequences
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Longtime01 | 2025-01-14 | 30.2g | 1799s | Long Time | [OneDrive]/[Alipan] | OneDrive/Alipan |
| Longtime02 | 2025-01-18 | 36.3g | 2118s | Long Time | [OneDrive]/[Alipan] | OneDrive/Alipan |
| Outdoor01 | 2025-01-03 | 6.10g | 411s | Outdoor | OneDrive/Alipan | OneDrive/Alipan |
| Outdoor04 | 2025-01-03 | 13.4g | 782s | Outdoor | OneDrive/Alipan | OneDrive/Alipan |
Figure 4. Light01 Sequences
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Dynamic01 | 2024-11-24 | 2.14g | 175s | Dynamic Person | OneDrive/Alipan | OneDrive/Alipan |
| Dynamic02 | 2024-11-24 | 1.85g | 150s | Dynamic Person | OneDrive/Alipan | OneDrive/Alipan |
| Occlusion01 | 2024-11-24 | 1.46g | 142s | Full Occlusion | OneDrive/Alipan | OneDrive/Alipan |
| Occlusion02 | 2024-11-24 | 1.48g | 144s | Full Occlusion | OneDrive/Alipan | OneDrive/Alipan |
| Varying-illu01 | 2024-11-24 | 1.84g | 154s | Varying Illumination | OneDrive/Alipan | OneDrive/Alipan |
| Varying-illu02 | 2024-11-24 | 1.75g | 146s | Varying Illumination | OneDrive/Alipan | OneDrive/Alipan |
| Dark03 | 2024-11-24 | 2.01g | 170s | Dark Room | OneDrive/Alipan | OneDrive/Alipan |
| Dark04 | 2024-11-24 | 1.90g | 161s | Dark Room | OneDrive/Alipan | OneDrive/Alipan |
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Dynamic03 | 2024-12-06 | 3.20g | 284s | Dynamic Person | OneDrive/Alipan | OneDrive/Alipan |
| Dynamic04 | 2024-12-06 | 4.32g | 384s | Dynamic Person | OneDrive/Alipan | OneDrive/Alipan |
| Occlusion03 | 2024-12-01 | 4.00g | 396s | Partial Occlusion | OneDrive/Alipan | OneDrive/Alipan |
| Occlusion04 | 2024-12-01 | 5.27g | 542s | Partial Occlusion | OneDrive/Alipan | OneDrive/Alipan |
| Varying-illu03 | 2025-1-13 | 13.5g | 1027s | Varying Illumination | OneDrive/Alipan | OneDrive/Alipan |
| Varying-illu04 | 2025-1-13 | 9.25g | 667s | Varying Illumination | OneDrive/Alipan | OneDrive/Alipan |
| Varying-illu05 | 2025-1-13 | 7.11g | 491s | Varying Illumination | OneDrive/Alipan | OneDrive/Alipan |
| Dark01 | 2024-11-25 | 2.21g | 206s | Night | OneDrive/Alipan | OneDrive/Alipan |
| Dark02 | 2024-11-25 | 7.57g | 710s | Night | OneDrive/Alipan | OneDrive/Alipan |
Figure 5. corridor01 Sequences
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Corridor01 | 2025-01-21 | 6.39g | 403s | Long Corridor | OneDrive/Alipan | OneDrive/Alipan |
| Corridor02 | 2025-01-21 | 4.62g | 293s | Long Corridor | OneDrive/Alipan | OneDrive/Alipan |
| Elevator01 | 2025-01-21 | 11.2g | 699s | Long Corridor,Elevator | OneDrive/Alipan | OneDrive/Alipan |
Figure 6. Wheelfloat01 Sequences
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Wheel-float01 | 2024-11-24 | 1.5g | 123s | Wheel Float | OneDrive/Alipan | OneDrive/Alipan |
| Wheel-float02 | 2024-11-24 | 1.84g | 149s | Wheel Float | OneDrive/Alipan | OneDrive/Alipan |
| Sha-turn01 | 2024-11-24 | 1.68g | 138s | Shap Turn | OneDrive/Alipan | OneDrive/Alipan |
| Sha-turn02 | 2024-11-24 | 1.22g | 100s | Shap Turn | OneDrive/Alipan | OneDrive/Alipan |
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| Grass01 | 2025-01-19 | 6.12g | 287s | Wheel Float | OneDrive/Alipan | OneDrive/Alipan |
| Grass02 | 2025-01-21 | 3.58g | 172s | Wheel Float | OneDrive/Alipan | OneDrive/Alipan |
| Z-Rough-Road01 | 2025-01-14 | 10.4g | 533s | Z Rough Road | OneDrive/Alipan | OneDrive/Alipan |
Figure 7. GNSS_Denied01 Sequences
| Sequence Name | Collection Date | Total Size | Duration | Features | Rosbag | GT |
|---|---|---|---|---|---|---|
| GNSS-denial01 | 2025-01-19 | 10.5g | 609s | Long time,GNSS Denial | OneDrive/Alipan | OneDrive/Alipan |
| GNSS-denial02 | 2025-01-21 | 12.7g | 750s | Long time,GNSS Denial | OneDrive/Alipan | OneDrive/Alipan |
⚠️ Known Issues:
- The RGB images collected by the D435i and X4 cameras are rolling shutter, which might affect the performance of some visual SLAM systems which require global shutter.
- The dataset lacks external trigger between sensors, instead, we perform synchronization via software synchronization.
We offer two download options for each M3DGR sequence: OneDrive (recommended) and Alipan(阿里云盘). If you use Alipan to download sequences as a folder, you need to double-click the 双击合并.bat file in the folder in Win10/Win11 to merge the original data automatically. Download all our public sequences at once through this link.
You can quickly get the trajectory in TUM format through the TF tree method like this.
- If the GT is obtained by RTK/Mocap, you can directly use evo to evaluate：
evo_ape tum GTDynamic01.txt Dynamic01.txt -ap
- If the GT is obtained by ArUco, you can use our script to evaluate：
pip install numpy colorama tabulate evo
python ArUco_evaluate.py <GT_file_path> <evaluation_dir_path> [options]
<GT_file_path>: GT file path. <evaluation_dir_path>: folder path where the tum format file to be evaluated is stored. [options]: -t: sort the results by translation error, -r: sort by rotation error, -a: sort by root mean square error. Default sorting is based on translation error. For example：
python ArUco_evaluate.py GTCorridor01.txt ./M3DGR
Our paper uses a more stringent translation error. The Tracking Rate is calculated based on the recording time and the actual trajectory change time. If the algorithm crashes prematurely or the trajectory stops updating, the Tracking Rate will be less than 100%.
We have tested following cutting-edge methods on M3DGR🦄 dataset with well-tuned parameters. We will release all these custom baseline codes upon paper acceptance!. The testing configuration is detailed below:
💡 Measurement
💡 VO system
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③ [PMLR2021] Tartanvo: A generalizable learning-based vo [paper][code][adapted_code]. (Sensors: D435I RGB camera)
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④ [T-RO2017] Orb-slam2: An open-source slam system for monocular, stereo, and rgb-d cameras [paper][code][adapted_code]. (Sensors: D435I RGB camera)
💡 VIO system
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⑤ [T-RO2021] Orb-slam3: An accurate open-source library for visual, visual–inertial, and multimap slam [paper][code][adapted_code]. (Sensors: D435I RGB camera and Realsense D435i IMU 6-axis)
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⑥ [RA-L2022] DM-VIO: Delayed marginalization visual-inertial odometry [paper][code][adapted_code]. (Sensors: D435I RGB camera and Realsense D435i IMU 6-axis)
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⑦ [T-RO2018] Vins-mono: A robust and versatile monocular visual-inertial state estimator [paper][code][adapted_code]. (Sensors: D435I RGB camera and Realsense D435i IMU 6-axis)
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⑧ [Sensors2019] VINS-RGBD: RGBD-inertial trajectory estimation and mapping for ground robots [paper][code][adapted_code]. (Sensors: D435I RGB camera and Realsense D435i IMU 6-axis)
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⑨ [T-RO2022] GVINS: Tightly coupled GNSS–visual–inertial fusion for smooth and consistent state estimation [paper][code][adapted_code]. (Sensors: D435I RGB camera, Realsense D435i IMU 6-axis and GNSS)
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⑩ [2021] VIW-Fusion: visual-inertial-wheel fusion odometry [code][adapted_code]. (Sensors: D435I RGB camera, Realsense D435i IMU 6-axis and WHEELTEC Wheel Odometer )
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⑪ [2021] VINS-GPS-Wheel: Visual-Inertial Odometry Coupled with Wheel Encoder and GNSS [code][adapted_code]. (Sensors: D435I RGB camera, Realsense D435i IMU 6-axis, WHEELTEC Wheel Odometer and GNSS )
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⑫ [ICRA2024] Ground-fusion: A low-cost ground slam system robust to corner cases [paper][code][adapted_code]. (Sensors: D435I RGB camera, Realsense D435i IMU 6-axis, WHEELTEC Wheel Odometer and GNSS )
💡 LO system
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⑬ [RSS2014] LOAM: Lidar odometry and mapping in real-time [paper][code][adapted_code]. (Sensors: Livox MID-360 )
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⑭ [ICRA2020] Loam livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV [paper][code][adapted_code]. (Sensors: Livox MID-360 )
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⑮ [2023] CTLO: Continuous-Time LiDAR Odometry [code][adapted_code]. (Sensors: Livox MID-360 )
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⑯ [IROS2018] Lego-loam: Lightweight and ground-optimized lidar odometry and mapping on variable terrain [paper][code][adapted_code]. (Sensors: Livox Avia )
💡 LIO system
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⑰ [ICRA 2019] LIO-mapping: Tightly coupled 3d lidar inertial odometry and mapping [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis)
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⑱ [IROS2020] Lio-sam: Tightly-coupled lidar inertial odometry via smoothing and mapping [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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⑲ [ICRA2020] Lins: A lidar-inertial state estimator for robust and efficient navigation [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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⑳ [RA-L2021] LiLi-OM: Towards high-performance solid-state-lidar-inertial odometry and mapping [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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㉑ [2021] LIO-Livox: A Robust LiDAR-Inertial Odometry for Livox LiDAR [code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis)
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㉒ [RA-L2022] Faster-LIO: Lightweight Tightly Coupled Lidar-Inertial Odometry Using Parallel Sparse Incremental Voxels [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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㉓ [2022] IESKF-LIO: reference to fast_lio1.0 [code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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㉔ [RA-L2022] VoxelMap: Efficient and probabilistic adaptive voxel mapping method for LiDAR odometry [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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㉕ [T-RO2022] Fast-lio2: Fast direct lidar-inertial odometry [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
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㉖ [AIS2023] Point-LIO: Robust High-Bandwidth Lidar-Inertial Odometry [paper][code][adaptde_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox MID-360 and Realsense D435i IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis / Livox Avia and Realsense D435i IMU 6-axis)
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㉗ [RA-L2023] LOG-LIO: A LiDAR-Inertial Odometry with Efficient Local Geometric Information Estimation [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis )
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㉘ [2023] CT-LIO: Continuous-Time LiDAR-Inertial Odometry [code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis )
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㉙ [ICRA2023] DLIO: Direct LiDAR-Inertial Odometry: Lightweight LIO with Continuous-Time Motion Correction [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis )
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㉚ [2023] HM-LIO: A Hash Map based LiDAR-Inertial Odometry [code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis )
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㉛ [T-IV2024] MM-LINS: a Multi-Map LiDAR-Inertial System for Over-Degenerate Environments [paper][code][adapted_code]. (Sensors: Livox Avia and Livox Avia IMU 6-axis)
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㉜ [T-RO2025] LIGO: Tightly Coupled LiDAR-Inertial-GNSS Odometry based on a Hierarchy Fusion Framework for Global Localization with Real-time Mapping [paper][code][adapted_code]. (Sensors: Livox MID-360 and Livox MID-360 IMU 6-axis / Livox Avia and Livox Avia IMU 6-axis)
💡 LVIO system
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㉝ [ICRA2021] LVI-SAM: Tightly-coupled Lidar-Visual-Inertial Odometry via Smoothing and Mapping [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Realsense D435i IMU 6-axis / Livox Avia, D435I RGB camera and Realsense D435i IMU 6-axis)
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㉞ [RA-L2021] R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Livox MID-360 IMU 6-axis / Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis)
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㉟ [ICRA2022] R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Livox MID-360 IMU 6-axis / Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis / Livox MID-360, D435I RGB camera and Realsense D435i IMU 6-axis / Livox Avia, D435I RGB camera and Realsense D435i IMU 6-axis)
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㊱ [IROS2022] FAST-LIVO: Fast and Tightly-coupled Sparse-Direct LiDAR-Inertial-Visual Odometry [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Livox MID-360 IMU 6-axis / Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis)
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㊲ [RA-L2023] Coco-LIC: Continuous-Time Tightly-Coupled LiDAR-Inertial-Camera Odometry using Non-Uniform B-spline [paper][code][adapted_code]. (Sensors: Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis)
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㊳ [RA-L2024] SR-LIVO: LiDAR-Inertial-Visual Odometry and Mapping with Sweep Reconstruction [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Livox MID-360 IMU 6-axis / Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis)
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㊴ [T-RO2024] FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry [paper][code][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera and Livox MID-360 IMU 6-axis / Livox Avia, D435I RGB camera and Livox Avia IMU 6-axis)
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㊵ [IROS2025] Ground-Fusion++: Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework [paper][adapted_code]. (Sensors: Livox MID-360, D435I RGB camera, Realsense D435i IMU 6-axis, WHEELTEC Wheel Odometer and GNSS)
⚠️ Known Issues:
- Please note that experimental performance may exhibit variability across runs and hardware platforms; the results reported in the paper represent averaged outcomes under our testing conditions.
- It is possible to further improve performance through careful parameter tuning and repeated evaluation in specific scenarios.
💡 VO system
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
💡 VIO system
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
💡 LO system
- [XXXX2025]Your Paper Name [[your paper link](TBD)][[your adapted code](TBD)] - [XXXX2025]Your Paper Name [[your paper link](TBD)][[your adapted code](TBD)]💡 LIO system
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
💡 LVIO system
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
- [XXXX2025]Your Paper Name [your paper link][your adapted code]
@article{zhang2025towards,
title={Towards Robust Sensor-Fusion Ground SLAM: A Comprehensive Benchmark and A Resilient Framework},
author={Zhang, Deteng and Zhang, Junjie and Sun, Yan and Li, Tao and Yin, Hao and Xie, Hongzhao and Yin, Jie},
journal={arXiv preprint arXiv:2507.08364},
year={2025}
}
@inproceedings{yin2024ground,
title={Ground-fusion: A low-cost ground slam system robust to corner cases},
author={Yin, Jie and Li, Ang and Xi, Wei and Yu, Wenxian and Zou, Danping},
booktitle={2024 IEEE International Conference on Robotics and Automation (ICRA)},
pages={8603--8609},
year={2024},
organization={IEEE}
}
@article{yin2021m2dgr,
title={M2dgr: A multi-sensor and multi-scenario slam dataset for ground robots},
author={Yin, Jie and Li, Ang and Li, Tao and Yu, Wenxian and Zou, Danping},
journal={IEEE Robotics and Automation Letters},
volume={7},
number={2},
pages={2266--2273},
year={2021},
publisher={IEEE}
}
We appreciate all contributions to improving M3DGR.
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Thanks to everyone for supporting this project.