Millimeter-wave (mmWave) radar is increasingly being considered as an alternative to optical sensors for robotic primitives like simultaneous localization and mapping (SLAM). While mmWave radar overcomes some limitations of optical sensors, such as occlusions, poor lighting conditions, and privacy concerns, it also faces unique challenges, such as missed obstacles due to specular reflections or fake objects due to multipath. To address these challenges, we propose Radarize, a self-contained SLAM pipeline that uses only a commodity single-chip mmWave radar. Our radar-native approach uses techniques such as Doppler shift-based odometry and multipath artifact suppression to improve performance. We evaluate our method on a large dataset of 146 trajectories spanning 4 buildings and mounted on 3 different platforms, totaling approximately 4.7 Km of travel distance. Our results show that our method outperforms state-of-the-art radar and radar-inertial approaches by approximately 5x in terms of odometry and 8x in terms of end-to-end SLAM, as measured by absolute trajectory error (ATE), without the need for additional sensors such as IMUs or wheel encoders.

翻译：摘要：毫米波雷达正逐渐被视为机器人基础任务（如同时定位与地图构建，SLAM）中光学传感器的替代方案。尽管毫米波雷达克服了光学传感器在遮挡、弱光条件和隐私问题等方面的局限性，但它也面临独特挑战，例如镜面反射导致的目标漏检或多径效应造成的虚假目标。为应对这些挑战，我们提出Radarize——一种仅使用商用单芯片毫米波雷达的独立SLAM流水线。该雷达原生方法采用多普勒频移里程计和多径伪影抑制等技术提升性能。我们在涵盖4栋建筑、3种不同平台、总计146条轨迹（约4.7公里行进距离）的大规模数据集上评估了该方法。结果表明，在不依赖IMU或轮式编码器等额外传感器的情况下，本方法在绝对轨迹误差（ATE）指标上，里程计性能优于现有最先进的雷达与雷达-惯性方法约5倍，端到端SLAM性能提升约8倍。