1 May 1991 INS integrated motion analysis for autonomous vehicle navigation
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Proceedings Volume 1521, Image Understanding for Aerospace Applications; (1991); doi: 10.1117/12.46056
Event: Munich '91 (Lasers '91), 1991, Munich, Germany
Abstract
This paper presents the results obtained from a maximally passive technique of obstacle detection for ground-based vehicles and rotorcraft. Automatic detection of these obstacles and the necessary guidance and control actions triggered by such detection would facilitate autonomous vehicle navigation. The approach to obstacle detection that is presented in this paper employs motion analysis of imagery generated by a passive sensor. Motion analysis of imagery obtained during vehicle travel is used to generate range measurements to world points within the field of view of the sensor, which can then be used to provide obstacle detection. Many types of existing vehicles contain an inertial navigation system (INS) which can be utilized to greatly improve the performance of motion analysis techniques and make them useful for practical military and civilian applications. Our motion analysis approach makes use of INS data to improve interest point selection, matching of the interest points, and the subsequent motion detection, tracking, and obstacle detection. In this paper we concentrate on the results of our processing when applied to sequences of lab and outdoor imagery. The range measurements that are made by INS integrated motion analysis are compared to a limited amount of ground truth that is available.
© (1991) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Barry A. Roberts, Michael E. Bazakos, "INS integrated motion analysis for autonomous vehicle navigation", Proc. SPIE 1521, Image Understanding for Aerospace Applications, (1 May 1991); doi: 10.1117/12.46056; https://doi.org/10.1117/12.46056
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KEYWORDS
Motion analysis

Image segmentation

Sensors

Image processing

Cameras

Aerospace engineering

Image understanding

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