论文标题
LIDAR级的定位与雷达？在不同环境中，CFEAR方法是准确，快速和稳健的大规模雷达探光仪
Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
论文作者
论文摘要
本文提出了一种使用旋转雷达进行大规模探测估算的准确，高效且无学习的方法，从经验上发现，可以很好地概括在非常多样化的环境中 - 从城市到林地，以及仓库和矿山的室内室内的户外环境，而无需更改参数。我们的方法将运动补偿与一对多扫描登记结合在一起，从而最大程度地减少了附近的面向表面点之间的距离，并以强大的损耗函数来减轻异常值。扩展了以前的方法CFEAR，我们对更广泛的数据集进行了深入的研究，从而量化了过滤，分辨率，注册成本和损失功能，密钥帧历史记录和运动补偿的重要性。我们提出了一种新的解决策略和配置，该策略和配置克服了稀疏性和偏见的先前问题，并将我们的最新时间提高了38％，因此，令人惊讶的是，表现优于雷达的大满贯，接近激光雷达大满贯。最准确的配置在牛津基准上的5Hz时达到了1.09％的误差，最快的误差在160Hz时达到1.79％的误差。
This paper presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments -- outdoors, from urban to woodland, and indoors in warehouses and mines - without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach CFEAR, we present an in-depth investigation on a wider range of data sets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar SLAM and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160Hz.
