RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions
RadarSLAM: A robust simultaneous localization and mapping system for all weather conditions
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DOI：
10.1177/02783649221080483
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发表时间：
2022-04
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影响因子：
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通讯作者：
Ziyang Hong;Y. Pétillot;Andrew M. Wallace;Sen Wang
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文献类型：
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作者：
Ziyang Hong;Y. Pétillot;Andrew M. Wallace;Sen Wang
同时定位和绘图（SLAM）系统必须具有鲁棒性，以支持长期的移动车辆和机器人应用。然而，当面对具有挑战性的照明或天气条件时，基于相机和激光雷达的SLAM系统可能会变得脆弱，这会降低图像和点云数据的效用。雷达的工作电磁频谱受环境变化的影响较小，虽然其独特的传感器模型和噪声特性在SLAM应用中带来了开放的挑战，但仍有发展前景。本文研究了调频连续波雷达在大规模室外环境下SLAM中的应用。我们提出了一个完整的雷达SLAM系统，包括一种新的雷达运动估计算法，该算法利用雷达几何结构进行可靠的特征跟踪。并通过关节优化对运动畸变进行最优补偿和姿态估计。它的闭环组件通过捕获和利用周围环境的结构信息，设计得简单而高效。在三个公共雷达数据集上进行了广泛的实验，从城市街道和居民区到农村和高速公路，与最先进的激光雷达、视觉和雷达方法相比，所提出的雷达SLAM系统具有竞争力的准确性和可靠性。结果表明，我们的系统在技术上是可行的，可以在辐射数据集的极端天气条件下实现可靠的SLAM，例如大雪和浓雾，展示了使用雷达进行全天候定位和测绘的潜力。
A Simultaneous Localization and Mapping (SLAM) system must be robust to support long-term mobile vehicle and robot applications. However, camera and LiDAR based SLAM systems can be fragile when facing challenging illumination or weather conditions which degrade the utility of imagery and point cloud data. Radar, whose operating electromagnetic spectrum is less affected by environmental changes, is promising although its distinct sensor model and noise characteristics bring open challenges when being exploited for SLAM. This paper studies the use of a Frequency Modulated Continuous Wave radar for SLAM in large-scale outdoor environments. We propose a full radar SLAM system, including a novel radar motion estimation algorithm that leverages radar geometry for reliable feature tracking. It also optimally compensates motion distortion and estimates pose by joint optimization. Its loop closure component is designed to be simple yet efficient for radar imagery by capturing and exploiting structural information of the surrounding environment. Extensive experiments on three public radar datasets, ranging from city streets and residential areas to countryside and highways, show competitive accuracy and reliability performance of the proposed radar SLAM system compared to the state-of-the-art LiDAR, vision and radar methods. The results show that our system is technically viable in achieving reliable SLAM in extreme weather conditions on the RADIATE Dataset, for example, heavy snow and dense fog, demonstrating the promising potential of using radar for all-weather localization and mapping.