Identifying Puddles based on Intensity Measurement using LiDAR
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LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, isused to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the drivingsurface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity informationto classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationalefor using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errorsin the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into differentgroups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoordriving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate themaximum width that could be crossed for a given vehicle body size and wheel radius.
Abstract
LiDAR, one of the most important sensing methods used in mobile robots and cars with assistive/autonomous driving functions, isused to locate surrounding obstacles or to build maps. For real-time path generation, the detection of potholes or puddles on the drivingsurface is crucial. To achieve this, we used the coordinates of the reflection points provided by LiDAR as well as the intensity informationto classify water areas, which was achieved by applying a linear regression method to the intensity distribution. The rationalefor using the LiDAR index as an input variable for linear regression is presented, and we demonstrated that it is not affected by errorsin the distance measurement value. Because of LiDAR vertical scanning, if the reflective surface is not uniform, it is divided into differentgroups according to the intensity distribution, and a mathematical basis for this is presented. Through experiments in an outdoordriving area, we could distinguish between flat ground, potholes, and puddles, and kinematic analysis was performed to calculate themaximum width that could be crossed for a given vehicle body size and wheel radius.
참고문헌 (8)
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