针对煤矿智能化建设对同时定位与建图（SLAM）技术的需求，以及现有SLAM技术在煤矿井下使用中因环境特征退化导致应用受限的问题，提出了一种适于煤矿井下的多传感器融合SLAM算法。算法由视觉里程计系统和激光SLAM系统2部分组成；视觉里程计系统由近红外相机与惯导传感器构成；激光SLAM系统基于特征点法激光SLAM框架，利用视觉里程计信息代替IMU预积分，并针对煤矿巷道结构改进激光点云特征分类方法，优化雷达帧间扫描匹配；在视觉里程计系统中设计异常处理机制，避免因点云特征退化造成IMU误差累计，导致定位建图失败。在煤矿模拟巷道中算法测试结果表明：算法能够在巷道环境中可靠运行，并且算法稳定性和鲁棒性相较现有SLAM算法有明显提升。

Simultaneous localization and mapping (SLAM) is a key technology to achieve coal mine intellectualization, but its usage is restricted in underground coal mines due to feature degradation. To address this problem, a multi-sensor fusion SLAM algorithm which is suitable for underground coal mines is proposed. It consists of two sub-systems: a visual odometry system and a lidar SLAM system. The visual odometry system is composed of a near-infrared camera and an inertial measurement unit (IMU) sensor. The laser SLAM system is based on the features-point laser SLAM framework, using visual odometer information instead of IMU pre-integration, and improving the laser point cloud feature classification method for coal mine roadway structure to optimize the radar frame scanning matching. The exception handling mechanism is designed in the vision odometer system to avoid IMU error accumulation caused by point cloud feature degradation, which leads to the failure of positioning and mapping. The test results of the algorithm in simulated roadway of the coal mine show that the algorithm can run reliably in the roadway environment, and the stability and robustness of the algorithm are significantly improved compared with the existing SLAM algorithm.