Sensor Data Fusion in Top-View Grid Maps using Evidential Reasoning with Advanced Conflict Resolution
This work addresses sensor fusion challenges for autonomous driving systems, representing an incremental improvement over existing methods.
The paper tackles the problem of fusing heterogeneous sensor data in top-view grid maps by improving conflict resolution with evidential reasoning and data-driven reliability estimation, resulting in robust combination and successful conflict resolution as demonstrated on the Kitti-360 dataset.
We present a new method to combine evidential top-view grid maps estimated based on heterogeneous sensor sources. Dempster's combination rule that is usually applied in this context provides undesired results with highly conflicting inputs. Therefore, we use more advanced evidential reasoning techniques and improve the conflict resolution by modeling the reliability of the evidence sources. We propose a data-driven reliability estimation to optimize the fusion quality using the Kitti-360 dataset. We apply the proposed method to the fusion of LiDAR and stereo camera data and evaluate the results qualitatively and quantitatively. The results demonstrate that our proposed method robustly combines measurements from heterogeneous sensors and successfully resolves sensor conflicts.