Real-time landmark-based optical vehicle self-location

Merrill D. Squires; Michael P. Whalen; Gary Moody; Charles J. Jacobus

doi:10.1117/12.267817

26 February 1997 Real-time landmark-based optical vehicle self-location

Merrill D. Squires, Michael P. Whalen, Gary Moody, Charles J. Jacobus

Proceedings Volume 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision; (1997) https://doi.org/10.1117/12.267817
Event: 25th Annual AIPR Workshop on Emerging Applications of Computer Vision, 1996, Washington, DC, United States

Abstract

This paper presents a system for performing real-time vehicular self-location through a combination of triangulation of target sightings and low-cost auxiliary sensor information (e.g. accelerometer, compass, etc.). The system primarily relies on the use of three video cameras to monitor a dynamic 180 degree field of view. Machine vision algorithms process the imagery from this field of view searching for targets placed at known locations. Triangulation results are then combined with the past video processing results and auxiliary sensor information to arrive at real-time vehicle location update rates in excess of 10 Hz on a single low-cost conventional CPU. To supply both extended operating range and nighttime operational capabilities, the system also possesses an active illumination mode that utilizes multiple, inexpensive infrared LEDs to act as the illuminating source for reflective targets. This paper presents the design methodology used to arrive at the system, explains the overall system concept and process flow, and will briefly discuss actual results of implementing the system on a standard commercial vehicle.

Citation Download Citation

Merrill D. Squires, Michael P. Whalen, Gary Moody, and Charles J. Jacobus "Real-time landmark-based optical vehicle self-location", Proc. SPIE 2962, 25th AIPR Workshop: Emerging Applications of Computer Vision, (26 February 1997); https://doi.org/10.1117/12.267817

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KEYWORDS

Sensors

Cameras

Global Positioning System

Imaging systems

Detection and tracking algorithms

Image processing

Machine vision

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