论文标题
Radarslam：所有天气都基于雷达的大规模猛击
RadarSLAM: Radar based Large-Scale SLAM in All Weathers
论文作者
论文摘要
在过去的十年中，使用不同的传感器方式提出了许多同时定位和映射（SLAM）算法。但是，在极端天气条件下强大的大满贯仍然是一个开放的研究问题。在本文中，提出了一个基于雷达的完整图形大量系统Radarslam在大规模环境中可靠的定位和映射。它由姿势跟踪，局部映射，环闭合检测和姿势图优化组成，并通过新颖的特征匹配和雷达图像上的概率点云生成增强。在公共雷达数据集和几个自我收集的雷达序列上进行了广泛的实验，证明了在各种不良天气条件（例如漆黑的夜晚，茂密的雾气和巨大的降雪量）中最先进的可靠性和定位准确性。
Numerous Simultaneous Localization and Mapping (SLAM) algorithms have been presented in last decade using different sensor modalities. However, robust SLAM in extreme weather conditions is still an open research problem. In this paper, RadarSLAM, a full radar based graph SLAM system, is proposed for reliable localization and mapping in large-scale environments. It is composed of pose tracking, local mapping, loop closure detection and pose graph optimization, enhanced by novel feature matching and probabilistic point cloud generation on radar images. Extensive experiments are conducted on a public radar dataset and several self-collected radar sequences, demonstrating the state-of-the-art reliability and localization accuracy in various adverse weather conditions, such as dark night, dense fog and heavy snowfall.
