Abstract
Occupancy grid map is a popular tool for representing the surrounding environments of mobile robots/intelligent vehicles. Its applications can be dated back to the 1980s, when researchers utilized sonar or LiDAR to illustrate environments by occupancy grids. However, in the literature, research on vision-based occupancy grid mapping is scant. Furthermore, when moving in a real dynamic world, traditional occupancy grid mapping is required not only with the ability to detect occupied areas, but also with the capability to understand dynamic environments. The paper addresses this issue by presenting a stereo-vision-based framework to create a dynamic occupancy grid map, which is applied in an intelligent vehicle driving in an urban scenario. Besides representing the surroundings as occupancy grids, dynamic occupancy grid mapping could provide the motion information of the grids. The proposed framework consists of two components. The first is motion estimation for the moving vehicle itself and independent moving objects. The second is dynamic occupancy grid mapping, which is based on the estimated motion information and the dense disparity map. The main benefit of the proposed framework is the ability of mapping occupied areas and moving objects at the same time. This is very practical in real applications. The proposed method is evaluated using real data acquired by our intelligent vehicle platform “SeTCar” in urban environments.
Highlights
In the field of intelligent vehicles, many tasks, such as localization, collision avoidance and path planning, are usually performed based on well-represented maps [1,2]
This paper proposes a framework of stereo-vision-based dynamic occupancy grid mapping in urban environments
We present a framework of a dynamic occupancy grid mapping technique
Summary
In the field of intelligent vehicles, many tasks, such as localization, collision avoidance and path planning, are usually performed based on well-represented maps [1,2]. This paper proposes a framework of stereo-vision-based dynamic occupancy grid mapping in urban environments. The proposed framework mainly comprises two components (motion analysis for the vehicle itself and independent moving objects and dynamic occupancy grid mapping) within two parallel processes (sparse feature points processing between two consecutive stereo image pairs and dense stereo processing). A novel independent moving object segmentation method based on a U-disparity map. Based on previous work in [6], we propose a dynamic occupancy grid mapping method with consideration for the pitch angle between the stereo-vision system and the ground plane.
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