Home > Papers from 2013 > Navigation with minimal visual information

Navigation with minimal visual information

This is a really interesting piece of robotics that has implications for insect vision. By asking how little information one needs in order to navigate, we essentially demonstrate that there is a massive amount of information in the low-resolution scenes perceived by insects.

Abstract
In this paper we use the algorithm SeqSLAM to address the question, how little and what quality of visual information is needed to localize along a familiar route? We conduct a comprehensive investigation of place recognition performance on seven datasets while varying image resolution (primarily 1 to 512 pixel images), pixel bit depth, field of view, motion blur, image compression and matching sequence length. Results confirm that place recognition using single images or short image sequences is poor, but improves to match or exceed current benchmarks as the matching sequence length increases. We then present place recognition results from two experiments where low-quality imagery is directly caused by sensor limitations; in one, place recognition is achieved along an unlit mountain road by using noisy, long-exposure blurred images, and in the other, two single pixel light sensors are used to localize in an indoor environment. We also show failure modes caused by pose variance and sequence aliasing, and discuss ways in which they may be overcome. By showing how place recognition along a route is feasible even with severely degraded image sequences, we hope to provoke a re-examination of how we develop and test future localization and mapping systems.

Milford, Michael. “Vision-based place recognition: how low can you go?.” The International Journal of Robotics Research 32.7 (2013): 766-789.

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Categories: Papers from 2013
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