3D Map Building for Mobile Robot Based on Multi-Sensor Fusion Aided SLAM

To deal effectively with uncertainty involved in 3D map building for mobile robot and enhance the accuracy, completeness and consistency of the map, this paper proposed a 3D simultaneous localization and mapping (SLAM) approach based on fusion of sensor data and Rao-Blackwellised particle filtering (RBPF). The sensor's uncertain probability model was established and noise involved in the sensor's data is dramatically removed by Bayesian filter. The sensor's information gathered from vision sensor and laser range finder was integrated by a sophisticated rule, and the 3D environment map with texture mapping was established in SLAM framework. As demonstrated from experimental results, the autonomous SLAM routine based on sensor fusion is qualified for building an elegant, complete and accurate environment model, which verifies the effectiveness of the presented approach.