A Robot Obstacle Avoidance Approach with LiDAR and RGB Camera Data Combined

Liu, Zesen; Guo, Chuanhong; Bi, Sheng; Yu, Kezheng; Peng, Guojun; Yue, Yuyang

doi:10.1109/cyber53097.2021.9588225

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…Fueled by the advances in deep learning, considerable effort has been put towards developing algorithms utilizing RGB and RGB-D data for free space identification and, subsequently, path planning and obstacle avoidance ( Li and Birchfield, 2010 ; Zhao et al, 2018 ; Seichter et al, 2021 ). Deep learning-based methods can be used stand-alone to reduce costs compared to a LiDAR sensor suite ( Messiou et al, 2022 ; Dang and Bui, 2023 ), or as a supervising method to improve the perception capabilities of mobile robots ( Juel et al, 2018 ; Liu et al, 2021 ; Arapis et al, 2023 ). Despite these advancements, certain challenges persist, including segmenting free space under varying floor conditions and further reducing computational requirements for real-time applications.…”

Section: Related Workmentioning

confidence: 99%

Efficient human 3D localization and free space segmentation for human-aware mobile robots in warehouse facilities

Arapis,

Jami,

Nalpantidis

2023

Front. Robot. AI

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Real-time prediction of human location combined with the capability to perceive obstacles is crucial for socially-aware navigation in robotics. Our work focuses on localizing humans in the world and predicting the free space around them by incorporating other static and dynamic obstacles. We propose a multi-task learning strategy to handle both tasks, achieving this goal with minimal computational demands. We use a dataset captured in a typical warehouse environment by mounting a perception module consisting of a Jetson Xavier AGX and an Intel L515 LiDAR camera on a MiR100 mobile robot. Our method, which is built upon prior works in the field of human detection and localization demonstrates improved results in difficult cases that are not tackled in other works, such as human instances at a close distance or at the limits of the field of view of the capturing sensor. We further extend this work by using a lightweight network structure and integrating a free space segmentation branch that can independently segment the floor space without any prior maps or 3D data, relying instead on the characteristics of the floor. In conclusion, our method presents a lightweight and efficient solution for predicting human 3D location and segmenting the floor space for low-energy consumption platforms, tested in an industrial environment.

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