Читать книгу Engineering Autonomous Vehicles and Robots - Shaoshan Liu - Страница 23

Computer vision can be utilized for both localization and active perception. For localization, we can rely on visual simultaneous localization and mapping (VSLAM) technologies to achieve accurate real-time vehicle locations. However, VSLAM usually suffers from cumulative errors such that the longer the distance the vehicle travels, the higher the localization error. Fortunately, by fusing VSLAM and GNSS localizations, we can achieve high accuracy under different conditions, because GNSS can be used as the group-truth data when it is not blocked, and VSLAM can provide high accuracy when GNSS is blocked.

In addition, computer vision can be used for active perception as well. Using stereo vision, we can extract spatial or depth information of different objects; using deep learning techniques, we can extract semantic information of different objects. By fusing spatial and semantic information, we can detect objects of interest, such as pedestrians and cars, as well as getting their distance to the current vehicle.