Terrain-Aided Navigation Using a Point Cloud Measurement Sensor

2025-10-07

AI Summary

To address the degradation of positioning accuracy in inertial navigation systems (INS) under GNSS-denied environments, this paper proposes a terrain-aided navigation method leveraging point cloud sensors. The method introduces two novel nonlinear observation models: a conventional ray-casting model and a computationally efficient sliding-grid model that eliminates the need for ray tracing. By fusing digital terrain model (DTM) data with real-time LiDAR point cloud scans, the approach generates high-accuracy observation innovation residuals, thereby enhancing observability of the altitude state and overcoming the performance limitations of radar altimeters. Experimental results demonstrate that the proposed method significantly improves positioning accuracy over conventional radar altimetry. Moreover, the sliding-grid model achieves comparable accuracy while reducing computational load substantially, making it particularly suitable for resource-constrained embedded platforms.

Technology Category

Intelligent Robots: State EstimationPlanning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Hardware-aware ML

Application Category

Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deploymentsGraph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphsSemantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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

Problem

• Developing terrain-aided navigation using point cloud sensors
• Comparing ray-casting and sliding grid measurement models
• Enhancing inertial navigation accuracy with point cloud data

Innovation

• Point cloud sensor aids inertial navigation system
• Two measurement models compared for terrain navigation
• Sliding grid method reduces computational intensity