By studying the fundamental characteristics of millimeter-wave radar data,we propose a millimeter-wave radar odometry autonomous localization technique.Our approach extracts AKAZE feature points from millimeter-wave radar images through feature matching.And we introduce three matching constraint conditions based on the geometric priors inherent in radar images,which significantly reduces feature point mismatches,ensuring the reliability of sensor pose and odometry calculation.Experimental results on the Oxford radar robot dataset show that our method achieves a relative positioning error of 6.8％for horizontal displacement and 0.89 degrees per meter for heading angle without the local mapping and loop closure detection optimization strategies,and the single-frame positioning time is only 0.081 s,which provides a new idea for indoor and outdoor navigation and positioning in harsh environments.
关键词
雷达里程计/自动驾驶/毫米波雷达数据处理/特征匹配/几何先验约束
Key words
radar odometry/automatic driving/millimeter-wave radar data processing/feature matching/geometric prior constraints
维普
万方数据