PDPose: indoor robot localization and obstacle avoidance based on visual object detection

Yu Meng; Qiang Qu

doi:10.1117/12.2656052

12 January 2023 PDPose: indoor robot localization and obstacle avoidance based on visual object detection

Yu Meng, Qiang Qu

Proceedings Volume 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022); 1250907 (2023) https://doi.org/10.1117/12.2656052
Event: Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 2022, Guangzhou, China

Abstract

Localization and obstacle avoidance are important problems for indoor robots. Visual-based localization (VBL) is a promising self-localization approach that identifies a device's location in a 3D space by using cameras to see the device's surrounding scenes and objects. In this paper, we present a pictorial planar surface based 3D object localization framework. However, the image shaking on moving robot leads to localization accuracy reducing. In order to improve the localization accuracy on moving robot, the depth information from RGBD camera is involved to correct the pose calculation. Furthermore, in order to produce a more acceptable decision on obstacle avoidance, we also design an optimal path planning using RGBD camera based object detection. We have built an autonomous moving robot that can self-localize using its on-board camera and the PDPose (Picture Depth Pose) technology. The experiment study shows that our localization methods are practical, have a very good accuracy, and can be used for real time robot navigation. Moreover, compared with the traditional obstacle method, the optimal obstacle method produces better path planting result.

Citation Download Citation

Yu Meng and Qiang Qu "PDPose: indoor robot localization and obstacle avoidance based on visual object detection", Proc. SPIE 12509, Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022), 1250907 (12 January 2023); https://doi.org/10.1117/12.2656052

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KEYWORDS

Cameras

Target detection

Detection and tracking algorithms

Visualization

RGB color model

Robot vision

Neural networks

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