Vision-based localization and mapping solution is promising to be adopted in the automated valet parking task. In this paper, a semantic SLAM framework that leverages the hybrid edge information on bird’s-eye view images is presented. To extract useful edges from the synthesized bird’s-eye view image and the free-space contours for the SLAM task, different segmentation methods are designed to remove the noisy glare edges and distorted object edges caused by the inverse perspective mapping in view synthesis. Since only the free-space segmentation model needs training, our methods can dramatically reduce the labeling burden compared with previous road marking based methods. Those incorrect and incomplete edges are further cleaned and recovered by a temporal fusion of consecutive edges in a local map, respectively. Both a semantic edge point cloud map and an occupancy grid map can be built simultaneously in real time. Experiments in a parking garage demonstrate that the proposed framework can achieve higher accuracy and perform more robustly than previous point feature based methods.

In this paper, we present a multi-camera visual odometry (VO) system for an autonomous vehicle. Our system mainly consists of a virtual LiDAR and a pose tracker. We use a perspective transformation method to synthesize a surroundview image from undistorted fisheye camera images. With a semantic segmentation model, the free space can be extracted. The scans of the virtual LiDAR are generated by discretizing the contours of the free space. As for the pose tracker, we propose a visual odometry system fusing both the feature matching and the virtual LiDAR scan matching results. Only those feature points located in the free space area are utilized to ensure the 2D-2D matching for pose estimation. Furthermore, bundle adjustment (BA) is performed to minimize the feature points reprojection error and scan matching error. We apply our system to an autonomous vehicle equipped with four fisheye cameras. The testing scenarios include an outdoor parking lot as well as an indoor garage. Experimental results demonstrate that our system achieves a more robust and accurate performance comparing with a fisheye camera based monocular visual odometry system.

This paper proposes a Hierarchical Task-guided Motion Planning (HTMP) scheme integrating Representation Space (R-space) model to address the motion planning problem in complex visually-guided tasks. The main characteristic of HTMP scheme is the interaction between task level and motion level. Such an interaction is based on the modification of R-space. When current R-space model and planner is unable to generate a feasible path, the dimension of R-space can be expanded to increase the flexibility of planning, which meets the extra requirements of natural motion. Simulation results verified the performance of the proposed method in various tasks fulfilled by a humanoid robot.