用点云数据提升惯性导航精度，比雷达高度计更准。
Terrain-Aided Navigation Using a Point Cloud Measurement Sensor
- 提出两种点云测量模型：射线投射法和滑动网格法。
- 滑动网格法计算量小，精度与射线投射法相当。
- 点云测量可显著提升导航精度，适合资源受限场景。
本文研究了利用点云测量实现地形辅助导航的可行性。目标是为惯性导航系统提供有效的非线性状态估计测量创新误差。比较了两种基于数字高程模型扫描的测量模型：一是基于给定姿态的典型射线投射法，生成该姿态下的预测点云；二是计算开销更低的滑动网格法，仅需姿态和点云模式即可生成预测点云。进一步分析了两种模型在高度可观测性方面的表现。以雷达高度计为基线，对比点云测量的性能，结果表明点云测量显著提升定位精度。结论指出，点云测量优于雷达高度计，具体模型选择取决于计算资源限制。
原文摘要 · Abstract (English)
We investigate the use of a point cloud measurement in terrain-aided navigation. Our goal is to aid an inertial navigation system, by exploring ways to generate a useful measurement innovation error for effective nonlinear state estimation. We compare two such measurement models that involve the scanning of a digital terrain elevation model: a) one that is based on typical ray-casting from a given pose, that returns the predicted point cloud measurement from that pose, and b) another computationally less intensive one that does not require raycasting and we refer to herein as a sliding grid. Besides requiring a pose, it requires the pattern of the point cloud measurement itself and returns a predicted point cloud measurement. We further investigate the observability properties of the altitude for both measurement models. As a baseline, we compare the use of a point cloud measurement performance to the use of a radar altimeter and show the gains in accuracy. We conclude by showing that a point cloud measurement outperforms the use of a radar altimeter, and the point cloud measurement model to use depends on the computational resources
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