5 research outputs found
Towards generalization of semi-supervised place classification over generalized Voronoi graph
With the progress of human-robot interaction (HRI), the ability of a robot to perform high-level tasks in complex environments is fast becoming an essential requirement. To this end, it is desirable for a robot to understand the environment at both geometric and semantic levels. Therefore in recent years, research towards place classification has been gaining in popularity. After the era of heuristic and rule-based approaches, supervised learning algorithms have been extensively used for this purpose, showing satisfactory performance levels. However, most of those approaches have only been trained and tested in the same environments and thus impede a generalized solution. In this paper, we have proposed a semi-supervised place classification over a generalized Voronoi graph (SPCoGVG) which is a semi-supervised learning framework comprised of three techniques: support vector machine (SVM), conditional random field (CRF) and generalized Voronoi graph (GVG), in order to improve the generalizability. The inherent problem of training CRF with partially labeled data has been solved using a novel parameter estimation algorithm. The effectiveness of the proposed algorithm is validated through extensive analysis of data collected in international university environments. © 2013 Elsevier B.V. All rights reserved
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
A Bayesian approach for place recognition
This paper presents a robust place recognition algorithm for mobile robots. The framework proposed combines nonlinear dimensionality reduction, nonlinear regression under noise, and variational Bayesian learning to create consistent probabilistic representations of places from images. These generative models are learnt from a few images and used for multi-class place recognition where classification is computed from a set of feature-vectors. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions and blurring. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition
A Bayesian approach for place recognition
This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition
A Bayesian approach for place recognition
This paper presents a robust place recognition algorithm for mobile robots that can be used for planning and navigation tasks. The proposed framework combines nonlinear dimensionality reduction, nonlinear regression under noise, and Bayesian learning to create consistent probabilistic representations of places from images. These generative models are incrementally learnt from very small training sets and used for multi-class place recognition. Recognition can be performed in near real-time and accounts for complexity such as changes in illumination, occlusions, blurring and moving objects. The algorithm was tested with a mobile robot in indoor and outdoor environments with sequences of 1579 and 3820 images, respectively. This framework has several potential applications such as map building, autonomous navigation, search-rescue tasks and context recognition