Real-time mobile robot navigation based on stereo vision and low-cost GPS

摘要

As most robot navigation systems for large-scale outdoor applications have been implemented based on high-end sensors, it is still challenging to implement a low-cost autonomous groundbased vehicle. This paper presents an autonomous navigation system using only a stereo camera and a low-cost GPS receiver. The proposed method consists of Visual Odometry (VO), pose estimation, obstacle detection, local path planning and a waypoint follower. VO computes a relative pose between two pairs of stereo images. However, VO inevitably suffers from drift (error accumulation) over time. A low-cost GPS provides absolute locations that can be used to correct VO drift. We fuse data from VO and GPS to achieve more accurate localization both locally and globally, using an Extended Kalman Filter (EKF). To detect obstacles, we use a dense depth map that is generated by stereo disparity estimation and transformed into a 2D occupancy grid map. Local path planning computes temporary waypoints to avoid obstacles, and a waypoint follower navigates the robot towards the goal point. We evaluated the proposed method with a mobile robot platform in real-time experiments in an outdoor environment. Experimental results show that the mobile vision and control system is capable of traversing roads in this outdoor environment autonomously.