Collaborative navigation is the most promising technique for infrastructure-free indoor navigation for a group of pedestrians, such as rescue personnel. Infrastructure-free navigation means using a system that is able to localize itself independent of any equipment pre-installed to the building using various sensors monitoring the motion of the user. The most feasible navigation sensors are inertial sensors and a camera providing motion information when a computer vision method called visual odometry is used. Collaborative indoor navigation sets challenges to the use of computer vision; navigation environment is often poor of tracked features, other pedestrians in front of the camera interfere with motion detection, and the size and cost constraints prevent the use of best quality cameras resulting in measurement errors. We have developed an improved computer vision based collaborative navigation method addressing these challenges using a depth (RGB-D) camera, a deep learning based detector to avoid using features found from other pedestrians and for controlling the inconsistency of object depth detection, which would degrade the accuracy of the visual odometry solution if not controlled. Our analysis show that our method improves the visual odometry solution using a low-cost RGB-D camera. Finally, we show the result for computing the solution using visual odometry and inertial sensor fusion for the individual and UWB ranging for collaborative navigation. ; Peer reviewed


    Access

    Download


    Export, share and cite



    Computer Vision-Based V2X Collaborative Perception

    Miucic, Radovan / Rajab, Samer | SAE Technical Papers | 2022


    Computer Vision-Based V2X Collaborative Perception

    Miucic, Radovan / Rajab, Samer | British Library Conference Proceedings | 2022


    Computer Vision-Based V2X Collaborative Perception

    Miucic, Radovan / Rajab, Samer | British Library Conference Proceedings | 2022


    Vision-based Perception for Autonomous Urban Navigation

    Bansal, Mayank / Das, Aveek / Kreutzer, Greg et al. | IEEE | 2008


    Indoor Navigation for a Complex Parking Building Based on Computer Vision

    Jia, Yulin / Jin, Zhihong / Zhang, Haiping et al. | IEEE | 2019