论文标题
映射雷达大满贯的扩展地标
Mapping Extended Landmarks for Radar SLAM
论文作者
论文摘要
使用汽车雷达传感器同时定位和映射（SLAM）可以为自主系统提供增强的传感功能。在SLAM应用程序中，对环境图的要求更大，有关地标范围的信息对于精确的导航和路径计划至关重要。尽管对象范围估计已成功地应用于目标跟踪，但由于传感器平台的额外不确定性，里程表读数的偏差以及测量非线性性，其对SLAM的适应性仍未得到解决。在本文中，我们建议将贝叶斯随机矩阵方法融合在一起，以估计雷达大满贯的地标。我们描述了实施里程碑范围初始化，预测和更新的详细信息。为了验证我们提出的方法的性能，我们将与无模型椭圆拟合算法进行比较，结果显示出更一致的范围估计。我们还证明，在州更新中利用具有里程碑意义的范围可以提高本地化准确性。
Simultaneous localization and mapping (SLAM) using automotive radar sensors can provide enhanced sensing capabilities for autonomous systems. In SLAM applications, with a greater requirement for the environment map, information on the extent of landmarks is vital for precise navigation and path planning. Although object extent estimation has been successfully applied in target tracking, its adaption to SLAM remains unaddressed due to the additional uncertainty of the sensor platform, bias in the odometer reading, as well as the measurement non-linearity. In this paper, we propose to incorporate the Bayesian random matrix approach to estimate the extent of landmarks in radar SLAM. We describe the details for implementation of landmark extent initialization, prediction and update. To validate the performance of our proposed approach we compare with the model-free ellipse fitting algorithm with results showing more consistent extent estimation. We also demonstrate that exploiting the landmark extent in the state update can improve localization accuracy.
