Precise localization relative to 3D Automated Driving map using the Decentralized Kalman filter with Feedback

TL;DR

This paper introduces a high-precision 3D localization method for automated driving using a Decentralized Kalman Filter with feedback, fusing multiple sensor inputs to achieve accurate vehicle positioning relative to complex 3D maps.

Contribution

It presents a novel 3D localization approach for AD that models vehicle motion with clothoids and fuses diverse sensor data using DKFF for enhanced accuracy.

Findings

Achieved precise 3D localization with multiple sensor fusion.

Effectively handled map features like polylines and landmarks.

Demonstrated robustness in complex 3D environments.

Abstract

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.