The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose ...The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision.Motivated by this,this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion.The algorithm integrates laser radar and Inertial Measurement Unit(IMU)data to construct the geometric structure map of the urban rail.It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information,thus generating high-precision maps.Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes,respectively,representing a 19.31%and 56.80%improvement compared to state-ofthe-art methods.展开更多
基金supported by the Beijing Natural Science Foundation(No.L221003).
文摘The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems.However,the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision.Motivated by this,this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion.The algorithm integrates laser radar and Inertial Measurement Unit(IMU)data to construct the geometric structure map of the urban rail.It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information,thus generating high-precision maps.Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033m for ground and tunnel scenes,respectively,representing a 19.31%and 56.80%improvement compared to state-ofthe-art methods.
