论文标题
快速mbym：利用傅立叶变换的翻译不变性，以高效，准确
Fast-MbyM: Leveraging Translational Invariance of the Fourier Transform for Efficient and Accurate Radar Odometry
论文作者
论文摘要
通过移动（MBYM）掩盖，通过在离散的姿势候选者之间进行详尽的相关搜索，提供了鲁棒的远程探光度测量。但是，这种密集的搜索产生了一个重要的计算瓶颈，当没有高端GPU时会阻碍实时性能。利用傅立叶变换的翻译不变性，在我们的方法中，我们将搜索角度和翻译搜索。通过保持端到端的不同性，神经网络可用于掩盖扫描，并通过直接监督姿势预测进行培训。训练更快，使用较少的内存，利用脱钩搜索可以使F-MBYM在CPU（168％）上实现显着的运行时性能改进，并在嵌入式设备上实时运行，与MBYM形成鲜明对比。在整个过程中，我们的方法在文献中可用的最佳雷达进气测变量保持准确和竞争力 - 在翻译中达到2.01％的终点漂移，而牛津雷达机器人数据集则达到了6.3度/km。
Masking By Moving (MByM), provides robust and accurate radar odometry measurements through an exhaustive correlative search across discretised pose candidates. However, this dense search creates a significant computational bottleneck which hinders real-time performance when high-end GPUs are not available. Utilising the translational invariance of the Fourier Transform, in our approach, f-MByM, we decouple the search for angle and translation. By maintaining end-to-end differentiability a neural network is used to mask scans and trained by supervising pose prediction directly. Training faster and with less memory, utilising a decoupled search allows f-MByM to achieve significant run-time performance improvements on a CPU (168%) and to run in real-time on embedded devices, in stark contrast to MByM. Throughout, our approach remains accurate and competitive with the best radar odometry variants available in the literature -- achieving an end-point drift of 2.01% in translation and 6.3deg/km on the Oxford Radar RobotCar Dataset.
