Environment Classification for Unmanned Ground Vehicles: Recognizing the Operational Environment


  1. Collier, J.
Corporate Authors
Defence R&D Canada - Suffield, Ralston ALTA (CAN)
Scientists at Defence R&D Canada – Suffield have been investigating autonomous operation of Unmanned Ground Vehicles (UGV)s in complex indoor and outdoor environments. The ability of the UGV to navigate autonomously is largely dependent on the accuracy and robustness of its perception system which seeks to create an accurate model of the UGV’s environment. One of the factors which determines the requirements for a UGV’s perception system is its operating environment. Designing a perception system capable of dealing with all types of environments is unfeasible at this time. In order to constrain the problem, many state-of-the-art UGVs are designed from the ground up to operate within an assumed environment. If these assumptions are valid, the UGV often operates effectively, however failure will occur when these environmental assumptions are incorrect. In order to alleviate this problem, it is desirable to have a perception system which can adapt to its changing environment. In order to do this, the UGV must understand the context of its environment and recognize when that context changes. One possible method of doing this is through the classification of video imagery. This paper discusses recent research in the area of indoor/outdoor classification for UGVs using computer vision and learning techniques. First, image features are extracted from video images which are labelled either indoor/outdoor. These features are then used as training inputs to a learning technique.

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Report Number
DRDC-SUFFIELD-TM-2007-275 — Technical Memorandum
Date of publication
01 Dec 2007
Number of Pages

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