针对传统视觉即时定位与地图构建（SLAM）中ORB特征存在的聚集问题，基于网格划分和关键点分层确定思想，设计了均匀FAST角点提取方法，进而设计了基于均匀分布的ORB特征结合暴力匹配的回环检测方法。与基于词袋（BoW）模型的回环检测算法对比实验表明，本文算法能显著提高回环检测的准确率。基于机器人操作系统（ROS）平台，将均匀ORB特征回环检测模块与直接稀疏里程计（DSO）相结合，设计了一种松耦合式的半直接法SLAM系统。实验结果表明，本文系统具有较高的地图构建性能。

Aiming to solve the aggregation problem of traditional uniform oriented FAST and rotated BRIEF（ORB） features in visual simultaneous localization and mapping（SLAM）， a uniform FAST corner extraction method is designed， which is based on grid division and laying to determinate key points. Furthermore， a corresponding loop detection method is designed based on uniform distribution of ORB features combined with brute force matching. Experiment results compared with BoW-based loop detection algorithms show that the proposed algorithm can significantly improve the accuracy of loop detection. Furthermore， a robot operating system（ROS） based loosely coupled semi-direct SLAM system is designed， which combine the uniform ORB feature loop detection module with direct sparse odometry（DSO）. The experimental results show that the proposed system has high map construction performance.