为测距-方位-俯仰传感器设计了更精准的噪声模型，提升定位精度。
An SE(3) Noise Model for Range-Azimuth-Elevation Sensors
- 基于SE(3)矩阵李群构建传感器噪声模型，融合外参与里程计不确定性
- 在水下激光扫描数据上验证，显著降低匹配估计的过自信问题
- 适合做高精度定位的科研人员和工程师参考
扫描匹配是状态估计中常用的技术。点云配准作为最流行的扫描匹配方法，是一个加权最小二乘问题，权重由测量点的逆协方差决定。若协方差表示不准确，将影响最小二乘的权重分配。例如，使用椭球形协方差来近似许多扫描传感器的曲线形、'香蕉状'噪声特征时，会导致最小二乘权重过于自信。此外，传感器到车辆的外部参数不确定性以及子图生成过程中的里程计不确定性，常被扫描匹配应用忽略，也可能导致匹配估计的过自信。本文通过在矩阵李群上为测距-方位-俯仰传感器建立噪声模型，实现外参与里程计不确定性的无缝融合。通过模拟示例及实际水下激光扫描获取的点云子图进行了验证。
原文摘要 · Abstract (English)
Scan matching is a widely used technique in state estimation. Point-cloud alignment, one of the most popular methods for scan matching, is a weighted least-squares problem in which the weights are determined from the inverse covariance of the measured points. An inaccurate representation of the covariance will affect the weighting of the least-squares problem. For example, if ellipsoidal covariance bounds are used to approximate the curved, "banana-shaped" noise characteristics of many scanning sensors, the weighting in the least-squares problem may be overconfident. Additionally, sensor-to-vehicle extrinsic uncertainty and odometry uncertainty during submap formation are two sources of uncertainty that are often overlooked in scan matching applications, also likely contributing to overconfidence on the scan matching estimate. This paper attempts to address these issues by developing a model for range-azimuth-elevation sensors on matrix Lie groups. The model allows for the seamless incorporation of extrinsic and odometry uncertainty. Illustrative results are shown both for a simulated example and for a real point-cloud submap collected with an underwater laser scanner.
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