Moving object segmentation (MOS) and Ego velocity estimation (EVE) are vital capabilities for mobile systems to achieve full autonomy. Several approaches have attempted to achieve MOSEVE using a LiDAR sensor. However, LiDAR sensors are typically expensive and susceptible to adverse weather conditions. Instead, millimeter-wave radar (MWR) has gained popularity in robotics and autonomous driving for real applications due to its cost-effectiveness and resilience to bad weather. Nonetheless, publicly available MOSEVE datasets and approaches using radar data are limited. Some existing methods adopt point convolutional networks from LiDAR-based approaches, ignoring the specific artifacts and the valuable radial velocity information of radar measurements, leading to suboptimal performance. In this paper, we propose a novel transformer network that effectively addresses the sparsity and noise issues and leverages the radial velocity measurements of radar points using our devised radar self- and cross-attention mechanisms. Based on that, our method achieves accurate EVE of the robot and performs MOS using only radar data simultaneously. To thoroughly evaluate the MOSEVE performance of our method, we annotated the radar points in the public View-of-Delft (VoD) dataset and additionally constructed a new radar dataset in various environments. The experimental results demonstrate the superiority of our approach over existing state-of-the-art methods. The code is available at https://github.com/ORCA-Uboat/RadarMOSEVE.

翻译：[translated abstract in Chinese] 移动目标分割（MOS）与自车速度估计（EVE）是移动系统实现完全自主的关键能力。已有多种方法尝试利用LiDAR传感器实现MOSEVE功能。然而，LiDAR传感器通常成本高昂且易受恶劣天气影响。相比之下，毫米波雷达（MWR）因其成本效益和抗恶劣天气的优势，已在机器人学和自动驾驶领域的实际应用中获得广泛关注。然而，当前公开可用的基于雷达数据的MOSEVE数据集与方法仍十分有限。部分现有方法直接沿用基于LiDAR的点卷积网络架构，未能有效处理雷达测量数据中的特定伪影以及宝贵的径向速度信息，导致性能欠佳。本文提出一种新型Transformer网络，通过我们设计的雷达自注意力与交叉注意力机制，有效解决了雷达数据的稀疏性和噪声问题，同时充分利用了雷达点的径向速度测量信息。基于此，该方法仅利用雷达数据即可同时实现机器人精准的EVE与MOS功能。为全面评估该方法在MOSEVE任务上的性能，我们对公开View-of-Delft（VoD）数据集中的雷达点进行了标注，并在多种环境下额外构建了一个新的雷达数据集。实验结果表明，本方法优于现有最先进技术。代码已开源：https://github.com/ORCA-Uboat/RadarMOSEVE。