Velocimeter Light-Detection-and-Ranging–Informed Terrain Relative Navigation
Abstract
Velocimeter light-detection-and-ranging (LIDAR)–informed batch and sequential estimation approaches for terrain relative navigation applications are developed in this paper. State-of-the-art velocimeter LIDAR sensors are capable of delivering simultaneous three-dimensional relative position, relative Doppler velocity, and reflectivity measurements for every point in the field of view. The proposed batch estimation algorithm yields accurate and statistically consistent vehicle velocity estimates in unknown (uncooperative) environments. This work presents novel pointwise relative position and Doppler velocity measurement models that can be utilized in the presence of known (cooperative) environments. Rate estimates obtained by the batch estimator are fused with the pointwise measurement models and classical inertial measurement unit formulations in the form of a multiplicative extended Kalman filter. This Doppler LIDAR-based sensor fusion approach yields informative terrain relative navigation solutions suitable for both cooperative and uncooperative environments. Results from extensive emulation robotics testing performed at NASA Johnson Space Center and Texas A&M’s Land, Air, and Space Robotics laboratory are presented to validate the proposed methodologies.
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Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection, path planning, and TRN even in the case of uncooperative operations.
“Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection20, path planning21, and TRN even in the case of uncooperative operations22.”
From Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning (2024) · cites this paper as [22]
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Probabilistic regression for autonomous terrain relative navigation via multi-modal feature learning
Kim, I., & Singh, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-81377-z · Cited by 6 · see citing works
“Real and simulated LiDAR data has proven efficient and robust data for neural network training in many applications, such as obstacle detection 20 , path planning 21 , and TRN even in the case of uncooperative operations 22 .”
Robotic Testbeds for Emulation of In-Space Assembly and Manufacturing of Advanced Structural Systems
McElreath, J., Capps, D., Adams, D. W., & Majji, M. (2025) · Journal of Manufacturing Science and Engineering · doi:10.1115/1.4068161 · Cited by 1 · see citing works
Self-citation
- Authors
- Weaver Adams, D., Peck, C., & Majji, M.
- Affiliations
- Texas A&M University
- Texas A&M University (US)
- Venue
- Journal of Guidance, Control, and Dynamics
- Published
- 2023-08
- Volume
- 46
- Issue
- 8
- Pages
- 1485-1500
- ISSN
- 0731-5090 (ISSN-L)
- 1533-3884
- OpenAlex
- W4365999071
- Cited by
- 2
- References
- 14
- Citations by year
- 2025: 1 · 2024: 1
- FWCI
- 0.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Guidance Control and Dynamics (publishedVersion)
- Funders
- NASA JPL SURP
- NASA Cooperative Agreement
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Inertial Sensor and Navigation
- Physical Sciences › Engineering › Aerospace Engineering › Robotics and Sensor-Based Localization
- Physical Sciences › Computer Science › Artificial Intelligence › Target Tracking and Data Fusion in Sensor Networks
- Keywords
- terrain relative navigation, Doppler velocity estimation, batch estimation, sequential estimation, multiplicative extended Kalman filter, sensor fusion, inertial measurement unit, relative position measurement, reflectivity measurement
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