2D Visual Place Recognition for Domestic Service Robots at Night

TL;DR

This paper introduces a passive, low-cost vision-based method for 2D localization of domestic service robots at night, utilizing contrast-normalized image matching and low light camera technology to operate in dark environments.

Contribution

It presents a novel night-time 2D localization approach combining daytime maps, image sequence matching, and low light cameras, avoiding intrusive sensors and beacons.

Findings

Effective 2D localization at night demonstrated in domestic environments.

Performance depends on odometry noise, sequence length, and interpolation.

Low light camera technology enables recognition in near-complete darkness.

Abstract

Domestic service robots such as lawn mowing and vacuum cleaning robots are the most numerous consumer robots in existence today. While early versions employed random exploration, recent systems fielded by most of the major manufacturers have utilized range-based and visual sensors and user-placed beacons to enable robots to map and localize. However, active range and visual sensing solutions have the disadvantages of being intrusive, expensive, or only providing a 1D scan of the environment, while the requirement for beacon placement imposes other practical limitations. In this paper we present a passive and potentially cheap vision-based solution to 2D localization at night that combines easily obtainable day-time maps with low resolution contrast-normalized image matching algorithms, image sequence-based matching in two-dimensions, place match interpolation and recent advances in conventional low light camera technology. In a range of experiments over a domestic lawn and in a lounge room, we demonstrate that the proposed approach enables 2D localization at night, and analyse the effect on performance of varying odometry noise levels, place match interpolation and sequence matching length. Finally we benchmark the new low light camera technology and show how it can enable robust place recognition even in an environment lit only by a moonless sky, raising the tantalizing possibility of being able to apply all conventional vision algorithms, even in the darkest of nights.