This paper investigates autonomous navigation for mobile robots with a low-cost monocular camera. The main challenge lies in: i) how to accurately detect prior unseen obstacles and acquire obstacles position from monocular images without depth information, and ii) how to get the constraints for path planning to generate safe and stable path for the robot. To accurately locate surrounding obstacles using only a monocular camera, we adopt a weakly-supervised semantic segmentation network trained from LIDAR data and perform inverse perspective transformation based on ground plane constraint. Meanwhile, to reduce segmentation noise, we establish a probability occupancy map based on the distance between robot and obstacles. For path generations, we present a novel search-and-optimization based planning approach to get boundary constraints in Frenet frame and generate stable local path with consecutive image inputs. In the simulation, segmentation Intersection over Union (IoU) of the drivable area achieves more than 99% and the average mapping accuracy is less than 10cm, showing feasibility and robustness of our navigation scheme.


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    Title :

    Autonomous Navigation for Mobile Robots with Weakly-Supervised Segmentation Network


    Contributors:
    Huang, Peinan (author) / Li, Jialun (author) / He, Jianping (author)


    Publication date :

    2022-09-01


    Size :

    4118457 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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