Environment-Aware Sensor Fusion for Obstacle Detection

Reliably detecting obstacles and identifying traversable areas is a key challenge in mobile robotics. For redundancy, information from multiple sensors is often fused. In this work we discuss how prior knowledge of the environment can improve the quality of sensor fusion, thereby increasing the performance of an obstacle detection module. We define a methodology to quantify the performance of obstacle detection sensors and algorithms. This information is used for environment-aware sensor fusion, where the fusion parameters are dependent on the past performance of each sensor in different parts of an operation site. The method is suitable for vehicles that operate in a known area, as is the case in many practical scenarios (warehouses, factories, mines, etc). The system is "trained" by manually driving the robot through a suitable trajectory along the operational areas of a site. The performance of a sensor configuration is then measured based on the similarity between the manually-driven trajectory and the trajectory that the path planner generates after detecting obstacles. Experiments are performed on an autonomous ground robot equipped with 2D laser sensors and a monocular camera with road detection capabilities. The results show an improvement in obstacle detection performance in comparison with a "naive" sensor fusion, illustrating the applicability of the method.