Radarize：利用可泛化的基于多普勒的里程计增强雷达SLAM
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
AI总结：
本文提出仅依赖单芯片毫米波雷达的SLAM流水线Radarize，通过多普勒里程计与多径抑制技术，在4.7公里数据集上实现比现有雷达方法提升约5倍里程计精度和8倍SLAM精度。
摘要：
毫米波（mmWave）雷达越来越被视为光学传感器在机器人原语（如同步定位与建图，SLAM）中的替代方案。虽然毫米波雷达克服了光学传感器的一些局限性，例如遮挡、光照条件差和隐私问题，但它也面临独特的挑战，例如由于镜面反射而漏检障碍物，或由于多径效应而产生虚假目标。为了解决这些挑战，我们提出了Radarize，一个仅使用商用单芯片毫米波雷达的自包含SLAM流水线。我们的雷达原生方法使用基于多普勒频移的里程计和多径伪影抑制等技术来提高性能。我们在一个包含146条轨迹、跨越4栋建筑并搭载在3种不同平台上的大型数据集上评估了我们的方法，总行驶距离约为4.7公里。我们的结果表明，以绝对轨迹误差（ATE）衡量，我们的方法在里程计方面优于最先进的雷达和雷达惯性方法约5倍，在端到端SLAM方面优于约8倍，且无需IMU或轮式编码器等额外传感器。
英文摘要：
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.
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