Real-time map refinement by fusing sonar and active stereo-vision

A good map of its environment is essential for efficient task execution of a mobile robot. Real time map update, especially in dynamic scenes is a difficult problem due to noisy sensor data and limited observation time. The paper describes a mapping procedure which identifies new obstacles in a scene and constructs a 3D surface model of it. This description is included in the geometrical map which robot navigation relies on. The mapping procedure is based on sonar range information and scenes reconstructed from stereo vision. The combination of sonar and stereo vision is advantageous, due to a complementary error characteristic concerning range and angular resolution. For sonar data integration the idea of local probability grids is proposed. Local grids which only cover areas where new obstacles are expected, reduce the complexity of grid based sonar data integration and can be applied to a dynamic environment. The partial models of an object that has been observed from different viewpoints are fused to a homogeneous description in a later step. A complex example shows the mapping procedure work robustly in dynamic indoor environments.