2 research outputs found
Intelligent Reference Curation for Visual Place Recognition via Bayesian Selective Fusion
A key challenge in visual place recognition (VPR) is recognizing places
despite drastic visual appearance changes due to factors such as time of day,
season, weather or lighting conditions. Numerous approaches based on
deep-learnt image descriptors, sequence matching, domain translation, and
probabilistic localization have had success in addressing this challenge, but
most rely on the availability of carefully curated representative reference
images of the possible places. In this paper, we propose a novel approach,
dubbed Bayesian Selective Fusion, for actively selecting and fusing informative
reference images to determine the best place match for a given query image. The
selective element of our approach avoids the counterproductive fusion of every
reference image and enables the dynamic selection of informative reference
images in environments with changing visual conditions (such as indoors with
flickering lights, outdoors during sunshowers or over the day-night cycle). The
probabilistic element of our approach provides a means of fusing multiple
reference images that accounts for their varying uncertainty via a novel
training-free likelihood function for VPR. On difficult query images from two
benchmark datasets, we demonstrate that our approach matches and exceeds the
performance of several alternative fusion approaches along with
state-of-the-art techniques that are provided with prior (unfair) knowledge of
the best reference images. Our approach is well suited for long-term robot
autonomy where dynamic visual environments are commonplace since it is
training-free, descriptor-agnostic, and complements existing techniques such as
sequence matching.Comment: 8 pages, 10 figures, accepted in the IEEE Robotics and Automation
Letter
Multiple map hypotheses for planning and navigating in non-stationary environments
This paper presents a method to enable a mobile robot working in non-stationary environments to plan its path and localize within multiple map hypotheses simultaneously. The maps are generated using a long-term and short-term memory mechanism that ensures only persistent configurations in the environment are selected to create the maps. In order to evaluate the proposed method, experimentation is conducted in an office environment. Compared to navigation systems that use only one map, our system produces superior path planning and navigation in a non-stationary environment where paths can be blocked periodically, a common scenario which poses significant challenges for typical planners