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 show that the sliding grid achieves comparable accuracy to raycasting, especially at higher altitudes, while being orders of magnitude faster computationally. By eliminating the need for raycasting, the sliding grid removes the dependency on dedicated GPU hardware or high-performance CPUs, making it well-suited for real-time terrain-aided navigation on resource-constrained embedded platforms.

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