Analysis of Robot Traversability over Unstructured Terrain using Information Fusion

Zhang, Wenyao1,2; Lyu, Shipeng1,2; Yao, Chen1,2; Xue, Feng1,2; Zhu, Zheng1,2; Jia, Zhenzhong1,2

10.1109/ICARM54641.2022.9959694

2022

7th IEEE International Conference on Advanced Robotics and Mechatronics

978-1-6654-8307-0

2022 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM 2022)

413-418

JUL 09-11, 2022

null,Guilin,PEOPLES R CHINA

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

Robot traversability is critical for its mission success, especially in unstructured environments, where the rough terrain can often lead to collision and tip-over. It is crucial for autonomous robots to analyze their traversability accurately and quickly. Most of past studies tend to use semantic or geometrical information alone, whose results are often not accurate enough since robots misclassify terrain or neglect other modal information. Some scholars have tried to fuse the semantic and geometry information, but they often require expensive sensors such as Lidar and industrial cameras, thereby limiting the applicability of these methods. Moreover, there is few robust methods of traversability analysis of robots over unstructured terrain. This paper proposes a novel and fast method of traversability analysis. Compared to the literature, the proposed method analyzes traversability based on semantic information, geometry information (e.g., slop, step height), and robot mobility. Moreover, this method only requires low-cost RGB-D cameras such as RealSense D435i rather than expensive sensors such as Lidar and industrial cameras. We also conduct multiple experiments to validate the proposed approach. The results prove that the robot can analyze traversability accurately even in the case of misclassified terrains.

Laser radar Mechatronics Service robots Semantics Robot vision systems Sensor fusion Cameras

First ; Corresponding

English

CPCI-S

Science, Technology and Innovation Commission of Shenzhen Municipality[ZDSYS20200811143601004]

Automation & Control Systems ; Engineering ; Robotics

Automation & Control Systems ; Engineering, Electrical & Electronic ; Robotics

WOS:000926398000067

Web of Science

1.Southern Univ Sci & Technol SUSTech, Dept Mech & Energy Engn, Shenzhen Key Lab Biomimet Robot & Intelligent Sys, Shenzhen 518055, Peoples R China
2.SUSTech, Guangdong Prov Key Lab Human Augmentat & Rehabil, Shenzhen 518055, Peoples R China

Zhang, Wenyao,Lyu, Shipeng,Yao, Chen,et al. Analysis of Robot Traversability over Unstructured Terrain using Information Fusion[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:413-418.