TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments

Yao，Chen1,2; Ge，Yangtao1,2; Shi，Guowei1,2; Wang，Zirui1,2; Yang，Ningbo1,2; Zhu，Zheng1,2; Wei，Hexiang3; Zhao，Yuntian1,2; Wu，Jing1,2; Jia，Zhenzhong1,2

2024-07-01

10.1109/LRA.2024.3407411

IEEE Robotics and Automation Letters   影响因子和分区

2377-3766

—Terrain-aware perception holds the potential to improve the robustness and accuracy of autonomous robot navigation in the wilds, thereby facilitating effective off-road traversals. However, the lack of multi-modal perception across various motion patterns hinders the solutions of Simultaneous Localization And Mapping (SLAM), especially when confronting non-geometric hazards in demanding landscapes. In this paper, we first propose a Terrain-Aware multI-modaL (TAIL) dataset tailored to deformable and sandy terrains. It incorporates various types of robotic proprioception and distinct ground interactions for the unique challenges and benchmark of multi-sensor fusion SLAM. The versatile sensor suite comprises stereo frame cameras, multiple ground-pointing RGB-D cameras, a rotating 3D LiDAR, an IMU, and an RTK device. This ensemble is hardware-synchronized, well-calibrated, and self-contained. Utilizing both wheeled and quadrupedal locomotion, we efficiently collect comprehensive sequences to capture rich unstructured scenarios. It spans the spectrum of scope, terrain interactions, scene changes, ground-level properties, and dynamic robot characteristics. We benchmark several state-of-the-art SLAM methods against ground truth and provide performance validations. Corresponding challenges and limitations are also reported.

deformable granular environments different types of robots multi-modal sensor fusion SLAM dataset

SCI ; EI

英语

第一 ; 通讯

20242316203517

Benchmarking ; Cameras ; Landforms ; Mapping ; Off road vehicles ; Robots ; Stereo image processing

Surveying:405.3 ; Geology:481.1 ; Radar Systems and Equipment:716.2 ; Data Processing and Image Processing:723.2 ; Robotics:731.5 ; Optical Devices and Systems:741.3 ; Photographic Equipment:742.2

2-s2.0-85194879902

Scopus

1.the Shenzhen Key Laboratory of Biomimetic Robotics and Intelligent Systems,Department of Mechanical and Energy Engineering,Southern University of Science and Technology (SUSTech),Shenzhen,518055,China
2.the Guangdong Provincial Key Laboratory of Human-Augmentation and Rehabilitation Robotics in Universities,SUSTech,Shenzhen,518055,China
3.the Department of Electronic and Computer Engineering,The Hong Kong University of Science and Technology,Hong Kong

Yao，Chen,Ge，Yangtao,Shi，Guowei,et al. TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments[J]. IEEE Robotics and Automation Letters,2024,9(7):6696-6703.

Yao，Chen.,Ge，Yangtao.,Shi，Guowei.,Wang，Zirui.,Yang，Ningbo.,...&Jia，Zhenzhong.(2024).TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments.IEEE Robotics and Automation Letters,9(7),6696-6703.

Yao，Chen,et al."TAIL: A Terrain-Aware Multi-Modal SLAM Dataset for Robot Locomotion in Deformable Granular Environments".IEEE Robotics and Automation Letters 9.7(2024):6696-6703.