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Vision-based positioning of electric buses for assisted docking to charging stations

Nowak Tomasz, Nowicki Michał R., Skrzypczyński Piotr

International Journal of Applied Mathematics and Computer Science

2022

Vol. 32, no. 4

583--599

We present a novel approach to vision-based localization of electric city buses for assisted docking to a charging station. The method assumes that the charging station is a known object, and employs a monocular camera system for positioning upon carefully selected point features detected on the charging station. While the pose is estimated using a geometric method and taking advantage of the known structure of the feature points, the detection of keypoints themselves and the initial recognition of the charging station are accomplished using neural network models. We propose two novel neural network architectures for the estimation of keypoints. Extensive experiments presented in the paper made it possible to select the MRHKN architecture as the one that outperforms state-of-the-art keypoint detectors in the task considered, and offers the best performance with respect to the estimated translation and rotation of the bus with a low-cost hardware setup and minimal passive markers on the charging station.

Comparison of keypoint detection methods for indoor and outdoor scene recognition

Libal U., Łoziuk Ł.

Journal of Automation Mobile Robotics and Intelligent Systems

2017

Vol. 11, No. 4

7--14

We describe an experimental study, based on several million video scenes, of seven keypoint detection algorithms: BRISK, FAST, GFTT, HARRIS, MSER, ORB and STAR. It was observed that the probability distributions of selected keypoints are drastically different between indoor and outdoor environments for all algorithms analyzed. This paper presents a simple method for distinguishing between indoor and outdoor environments in a video sequence. The proposed method is based on the central location of keypoints in video frames. This has lead to a universally effective indoor/outdoor environment recognition method, and may prove to be a crucial step in the design of robotic control algorithms based on computer vision, especially for autonomous mobile robots.