Precise localization relative to 3D Automated Driving map using the Decentralized Kalman filter with Feedback
This addresses the problem of accurate vehicle positioning in non-flat environments for automated driving systems, representing an incremental improvement in sensor fusion methods.
The paper tackles high-precision 3D localization for automated driving by modeling vehicle motion with clothoids and using a Decentralized Kalman filter with feedback to fuse multiple sensor inputs, achieving precise localization relative to a 3D map.
This paper represents the novel high precision localization approach for Automated Driving (AD) relative to 3D map. The AD maps are not necessarily flat. Hence, the problem of localization is solved here in 3D. The vehicle motion is modeled as piecewise planner but with vertical curvature which is approximated with clothoids. The localization problem is solved with Decentralized Kalman filter with feedback (DKFF) by fusing all available information. The odometry, visual odometry, GPS, the different sensor and mono camera inputs are fused together to obtain the precise localization relative to map. Polylines and landmarks from the map are dealt in the same way because of the line - point geometrical duality. A set of weak filters are accumulated in the strong tracking approach leading to the precise localization results.