DeRO：基于雷达里程计与加速度计辅助的航位推算用于机器人定位
DeRO: Dead Reckoning Based on Radar Odometry With Accelerometers Aided for Robot Localization
AI总结：
本文提出DeRO方法，直接利用4D FMCW雷达多普勒速度与陀螺仪数据进行航位推算，并结合重力倾斜角和雷达扫描匹配进行卡尔曼滤波更新，同时补偿雷达速度比例因子，在五个真实数据集上相比最先进雷达-惯性融合方法平均降低62%位置误差和66%旋转误差。
摘要：
本文提出了一种雷达里程计结构，直接利用雷达速度测量进行航位推算，同时保持其在卡尔曼滤波框架内更新估计的能力。具体而言，我们采用4D调频连续波（FMCW）雷达获得的多普勒速度，结合陀螺仪数据来计算位姿。该方法有助于减轻由加速度计偏差和双重积分引起的高漂移。相反，利用重力测量的倾斜角以及来自雷达扫描匹配的相对距离测量用于滤波器的测量更新。此外，为了进一步提高系统精度，我们估计并补偿雷达速度比例因子。所提方法的性能通过五个真实世界开源数据集进行了验证。结果表明，在绝对轨迹误差方面，与最先进的雷达-惯性融合方法相比，我们的方法平均将位置误差降低了62%，旋转误差降低了66%。
英文摘要：
In this paper, we propose a radar odometry structure that directly utilizes radar velocity measurements for dead reckoning while maintaining its ability to update estimations within the Kalman filter framework. Specifically, we employ the Doppler velocity obtained by a 4D Frequency Modulated Continuous Wave (FMCW) radar in conjunction with gyroscope data to calculate poses. This approach helps mitigate high drift resulting from accelerometer biases and double integration. Instead, tilt angles measured by gravitational force are utilized alongside relative distance measurements from radar scan matching for the filter's measurement update. Additionally, to further enhance the system's accuracy, we estimate and compensate for the radar velocity scale factor. The performance of the proposed method is verified through five real-world open-source datasets. The results demonstrate that our approach reduces position error by 62% and rotation error by 66% on average compared to the state-of-the-art radar-inertial fusion method in terms of absolute trajectory error.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
