作者
Nicholas Charron, Stephen Phillips, Steven L Waslander
发表日期
2018/5/8
研讨会论文
2018 15th Conference on Computer and Robot Vision (CRV)
页码范围
254-261
出版商
IEEE
简介
A common problem in autonomous driving is designing a system that can operate in adverse weather conditions. Falling rain and snow tends to corrupt sensor measurements, particularly for lidar sensors. Surprisingly, very little research has been published on methods to de-noise point clouds which are collected by lidar in rainy or snowy weather conditions. In this paper, we present a method for removing snow noise by processing point clouds using a 3D outlier detection algorithm. Our method, the dynamic radius outlier removal filter, accounts for the variation in point cloud density with increasing distance from the sensor, with the goal of removing the noise caused by snow while retaining detail in environmental features (which is necessary for autonomous localization and navigation). The proposed method outperforms other noise-removal methods, including methods which operate on depth image …
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N Charron, S Phillips, SL Waslander - 2018 15th Conference on Computer and Robot Vision …, 2018