2018/01/02 by Varun Murali, Murali, Varun, Han-Pang Chiu +6
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Robotic Path Planning Algorithms #Robotics and Sensor-Based Localization
paper · pdf · doi:10.48550/arxiv.1801.00858
openalex publication_date 2018/01/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper presents a new approach for integrating semantic information for vision-based vehicle navigation. Although vision-based vehicle navigation systems using pre-mapped visual landmarks are capable of achieving submeter level accuracy in large-scale urban environment, a typical error source in this type of systems comes from the presence of visual landmarks or features from temporal objects in the environment, such as cars and pedestrians. We propose a gated factor graph framework to use semantic information associated with visual features to make decisions on outlier/ inlier computation from three perspectives: the feature tracking process, the geo-referenced map building process, and the navigation system using pre-mapped landmarks. The class category that the visual feature belongs to is extracted from a pre-trained deep learning network trained for semantic segmentation. The feasibility and generality of our approach is demonstrated by our implementations on top of two vision-based navigation systems. Experimental evaluations validate that the injection of semantic information associated with visual landmarks using our approach achieves substantial improvements in accuracy on GPS-denied navigation solutions for large-scale urban scenarios