Ground truth data plays an important role in validating perception algorithms and in developing data-driven models. Yet, generating ground truth data is a challenging process, often requiring tedious manual work. Thus, we present a post-processing approach to automatically generate ground truth data from environment sensors. In contrast to existing approaches, we incorporate raw sensor data from multiple vehicles. As a result, our cooperative fusion approach overcomes drawbacks of occlusions and decreasing sensor resolution with distance. To improve the alignment precision for raw sensor data fusion, we include mutual detections and match the jointly-observed static environment to support differential global positioning system localization. We further provide a new registration algorithm, where all point clouds are moved simultaneously, while restricting the transformation parameters to increase the robustness against misalignments. The benefits of our raw sensor data fusion approach are demonstrated with real lidar data from two test vehicles in different scenarios.     «

Ground truth data plays an important role in validating perception algorithms and in developing data-driven models. Yet, generating ground truth data is a challenging process, often requiring tedious manual work. Thus, we present a post-processing approach to automatically generate ground truth data from environment sensors. In contrast to existing approaches, we incorporate raw sensor data from multiple vehicles. As a result, our cooperative fusion approach overcomes drawbacks of occlusions and...     »