Estimation of Vehicle Pose and Position with Monocular Camera at Urban Road Intersections

Abstract

With the rapid development of urban, the scale of the city is expanding day by day. The road environment is becoming more and more complicated. The vehicle ego-localization in complex road environment puts forward imperative requirements for intelligent driving technology. The reliable vehicle ego-localization, including the lane recognition and the vehicle position and attitude estimation, at the complex traffic intersection is significant for the intelligent driving of the vehicle. In this article, we focus on the complex road environment of the city, and propose a pose and position estimation method based on the road sign using only a monocular camera and a common GPS (global positioning system). Associated with the multi-sensor cascade system, this method can be a stable and reliable alternative when the precision of multi-sensor cascade system decreases. The experimental results show that, within 100 meters distance to the road signs, the pose error is less than 2 degrees, and the position error is less than one meter, which can reach the lane-level positioning accuracy. Through the comparison with the Beidou high-precision positioning system L202, our method is more accurate for detecting which lane the vehicle is driving on.

Real‐time keypoints detection for autonomous recovery of the unmanned ground vehicle

“…However, GPS receivers often suffer from the lack of positioning accuracy, availability, and continuity due to the insufficient number of visible satellites in the tunnel, indoor environments, and multipath errors in the urban area [17, 18]. Current civilian GPS positioning technology is difficult to achieve centimetre‐level positioning accuracy [19], and therefore cannot meet the high‐precision requirements of the small UGV positioning. Therefore, GPS is suitable for navigation in a large area but not for the precise guidance of the small ground vehicle during the autonomous recovery process.…”

Low-Cost Precise Vehicle Localization Using Lane Endpoints and Road Signs for Highway Situations

“…However, its performance can be degraded by appearance changes of surrounding obstacles and it needs a large amount of storage for the digital map to contain numerous feature points and their descriptors. The road facilitybased approach extracts road facilities such as road surface markings [19]- [24], [41], traffic lights [18], traffic signs [25], road signs [17], [40], and streetlights [26] and matches them with the corresponding road facilities contained in the digital map. This approach requires a small amount of storage for the digital map compared with the feature point-based approach because the digital map contains simplified shapes, types, and locations of the road facilities.…”