低成本激光和视觉相结合的同步定位与建图研究

激光雷达和视觉传感是目前两种主要的服务机器人定位与导航技术，但现有的低成本激光雷 达定位精度较低且无法实现大范围闭环检测，而单独采用视觉手段构建的特征地图又不适用于导航应用。因此，该文以配备低成本激光雷达与视觉传感器的室内机器人为研究对象，提出了一种激光和视觉相结合的定位与导航建图方法：通过融合激光点云数据与图像特征点数据，采用基于稀疏姿态调整的优化方法，对机器人位姿进行优化。同时，采用基于视觉特征的词袋模型进行闭环检测，并进一步优化基于激光点云的栅格地图。真实场景下的实验结果表明，相比于单一的激光或视觉定位建图方 法，基于多传感器数据融合的方法定位精度更高，并有效地解决了闭环检测问题。

Research on Simultaneous Localization and Mapping Fusion with Low-Cost Laser Sensors and Vision

Laser lidar and vision sensors are the two mainstream three-dimensional sensing techniques in the applications of robot location and navigation. However, existing low-cost laser lidar usually has low location accuracy and cannot achieve loop closure detection in large areas. In this paper, an indoor robot equipped with low-cost laser lidar and camera was used for experiment. And a novel localization and mapping method was introduced by combing both lidar and image information. An optimization method based on sparse pose adjustment was used to optimize the robot pose by fusing laser points cloud and image feature points as constraints. At the same time, the bag of words model based on visual features was used for loop closure detection. The grid map was optimized by loop closure constraints. Real experimental results show that, the proposed method has better localization accuracy than either laser lidar or vision sensors, and loop closure detection also can be realized.