基于多源融合式SLAM的机器人三维环境建模

为有效处理移动机器人三维环境地图创建过程的不确定误差,提高所建地图的准确性、完整性和一致性,本文提出了一种基于传感器信息融合和Rao-Blackwellised粒子滤波(RBPF)的移动机器人三维同时定位与地图创建 (SLAM)方法. 在建立传感器不确定性概率模型的基础上,通过贝叶斯滤波实现传感器数据的去噪,将激光与视觉传感器获取的环境信息在一定的规则下融合,在SLAM框架下实现具有纹理映射的三维环境地图创建. 实验结果表明所用方法的有效性. 多源融合式自主SLAM提供了更为丰富、完备、准确的环境模型. 

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.