Скачать или смотреть Skyline-Based Localisation for Aggressively Manoeuvring Robots Using UV Sensors...

Place recognition is a key capability for navigating robots. While significant advances have been achieved on large, stable platforms such as robot cars, achieving robust performance on rapidly manoeuvring platforms in outdoor natural conditions remains a challenge, with few systems able to deal with both variable conditions and large tilt variations caused by rough terrain. Taking inspiration from biology, we propose a novel combination of sensory modality and image processing to obtain a significant improvement in the robustness of sequence-based image matching for place recognition. We use a UV-sensitive fisheye lens camera to segment sky from ground, providing illumination invariance, and encode the resulting binary images using spherical harmonics to enable rotation-invariant image matching. In combination, these methods also produce substantial pitch and roll invariance, as the spherical harmonics for the sky shape are minimally affected, providing the sky remains visible. We evaluate the performance of our method against a leading appearance-invariant technique (SeqSLAM) and a leading viewpoint-invariant technique (FAB-MAP 2.0) on three new outdoor datasets encompassing variable robot heading, tilt and lighting conditions in both forested and urban environments. The system demonstrates improved condition- and tilt-invariance, enabling robust place recognition during aggressive zigzag manoeuvring along bumpy trails and at tilt angles of up to 60 degrees.

Paper details:

http://ieeexplore.ieee.org/abstract/d...

Thomas Stone, Dario Differt, Michael Milford and Barbara Webb, "Skyline-Based Localisation for Aggressively Manoeuvring Robots Using UV Sensors and Spherical Harmonics", in IEEE International Conference on Robotics and Automation, 2016.

Thomas Stone and Barbara Webb are at the University of Edinburgh
Dario Differt is at the University of Bielefeld
Michael Milford is at the Queensland University of Technology

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