Abstract
Occupancy grid is a popular environment model that is widely applied for autonomous navigation of mobile robots. This model encodes obstacle information into the grid cells as a reference of the space state. However, when navigating on roads, the planning module of an autonomous vehicle needs to have semantic understanding of the scene, especially concerning the accessibility of the driving space. This paper presents a grid-based evidential approach for modeling semantic road space by taking advantage of a prior map that contains lane-level information. Road rules are encoded in the grid for semantic understanding. Our approach focuses on dealing with the localization uncertainty, which is a key issue, while parsing information from the prior map. Readings from an exteroceptive sensor are as well integrated in the grid to provide real-time obstacle information. All the information is managed in an evidential framework based on Dempster–Shafer theory. Real road results are reported with qualitative evaluation and quantitative analysis of the constructed grids to show the performance and the behavior of the method for real-time application.
Highlights
Grid-based environment modeling has become a popular perception paradigm since its first introduction in [1]
We propose to define spatial grids in which one can interpret the lane information as cell values which have semantic meanings needed by the trajectory planner of the navigation system
Bird views of the occupancy grids, lane grids, and combination grids are shown in the top row
Summary
Grid-based environment modeling has become a popular perception paradigm since its first introduction in [1]. There exists a large literature regarding to grid-based environment modeling for robotics applications and for autonomous vehicles. In [4], the authors have presented an occupancy-elevation grid mapping technique for autonomous navigation application. In [5], the occupany grid is constructed to identify different shape of objects by applying sensor fusion. In [7], the authors introduced an advanced occupancy grid approach to enable the robust separation of moving and stationary objects. In [8], a generic architecture for perception in dynamic outdoor environment by applying grid-based SLAM (Simultaneous Localization and Mapping) is presented. In [9], a grid-based
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