LiDAR Measurement Analysis in Range Domain
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초록
Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However,several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing iscrucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method forestimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead ofpoint clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithmwas verified by experiments in various environments.
Abstract
Light detection and ranging (LiDAR), a widely used sensor in mobile robots and autonomous vehicles, has its most important function as measuring the range of objects in three-dimensional space and generating point clouds. These point clouds consist of the coordinates of each reflection point and can be used for various tasks, such as obstacle detection and environment recognition. However,several processing steps are required, such as three-dimensional modeling, mesh generation, and rendering. Efficient data processing iscrucial because LiDAR provides a large number of real-time measurements with high sampling frequencies. Despite the rapid development of controller computational power, simplifying the computational algorithm is still necessary. This paper presents a method forestimating the presence of curbs, humps, and ground tilt using range measurements from a single horizontal or vertical scan instead ofpoint clouds. These features can be obtained by data segmentation based on linearization. The effectiveness of the proposed algorithmwas verified by experiments in various environments.
참고문헌 (11)
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