Map-aided Fusion Using Evidential Grids for Mobile Perception in Urban Environment
This work addresses perception challenges for autonomous vehicles in urban settings, but it is incremental as it builds on existing evidential grid methods.
The paper tackled the problem of mobile object perception in urban environments by fusing prior map knowledge with sensor data using evidential grids, resulting in improved distinction between stationary and mobile objects through a specialized counter and contextual discounting.
Evidential grids have been recently used for mobile object perception. The novelty of this article is to propose a perception scheme using prior map knowledge. A geographic map is considered an additional source of information fused with a grid representing sensor data. Yager's rule is adapted to exploit the Dempster-Shafer conflict information at large. In order to distinguish stationary and mobile objects, a counter is introduced and used as a factor for mass function specialisation. Contextual discounting is used, since we assume that different pieces of information become obsolete at different rates. Tests on real-world data are also presented.