Terrain-Aided Navigation Using a Point Cloud Measurement Sensor
Abdülbaki Şanlan, Fatih Erol, Murad Abu-Khalaf, Emre Koyuncu
TL;DR
Terrain-aided navigation is enhanced by leveraging a point-cloud measurement of surrounding terrain to aid an INS. The authors compare a ray-casting point-cloud prediction with a fast sliding-grid approach within a marginalized particle-filter framework and benchmark against radar altimetry. They demonstrate improved localization accuracy and altitude observability with point-cloud data, while highlighting a trade-off between computational cost and matching fidelity. The work suggests that point-cloud-based terrain sensing can outperform single-point altimetry, with the preferred model depending on available computational resources.
Abstract
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