Incremental spectral clustering and its application to topological mapping

This paper presents a novel use of spectral clustering algorithms to support cases where the entries in the affinity matrix are costly to compute. The method is incremental – the spectral clustering algorithm is applied to the affinity matrix after each row/column is added – which makes it possible to inspect the clusters as new data points are added. The method is well suited to the problem of appearance-based, on-line topological mapping for mobile robots. In this problem domain, we show that we can reduce environment-dependent parameters of the clustering algorithm to just a single, intuitive parameter. Experimental results in large outdoor and indoor environments show that we can close loops correctly by computing only a fraction of the entries in the affinity matrix. The accompanying video clip shows how an example map is produced by the algorithm

Incremental topological mapping using omnidirectional vision

This paper presents an algorithm that builds topological maps, using omnidirectional vision as the only sensor modality. Local features are extracted from images obtained in sequence, and are used both to cluster the images into nodes and to detect links between the nodes. The algorithm is incremental, reducing the computational requirements of the corresponding batch algorithm. Experimental results in a complex, indoor environment show that the algorithm produces topologically correct maps, closing loops without suffering from perceptual aliasing or false links. Robustness to lighting variations was further demonstrated by building correct maps from combined multiple datasets collected over a period of 2 month

Map Building and Monte Carlo Localization Using Global Appearance of Omnidirectional Images

In this paper we deal with the problem of map building and localization of a mobile robot in an environment using the information provided by an omnidirectional vision sensor that is mounted on the robot. Our main objective consists of studying the feasibility of the techniques based in the global appearance of a set of omnidirectional images captured by this vision sensor to solve this problem. First, we study how to describe globally the visual information so that it represents correctly locations and the geometrical relationships between these locations. Then, we integrate this information using an approach based on a spring-mass-damper model, to create a topological map of the environment. Once the map is built, we propose the use of a Monte Carlo localization approach to estimate the most probable pose of the vision system and its trajectory within the map. We perform a comparison in terms of computational cost and error in localization. The experimental results we present have been obtained with real indoor omnidirectional images

Markerless monocular tracking system for guided external eye surgery

This paper presents a novel markerless monocular tracking system aimed at guiding ophthalmologists during external eye surgery. This new tracking system performs a very accurate tracking of the eye by detecting invariant points using only textures that are present in the sclera, i.e., without using traditional features like the pupil and/or cornea reflections, which remain partially or totally occluded in most surgeries. Two known algorithms that compute invariant points and correspondences between pairs of images were implemented in our system: Scalable Invariant Feature Transforms (SIFT) and Speed Up Robust Features (SURF). The results of experiments performed on phantom eyes show that, with either algorithm, the developed system tracks a sphere at a 360◦ rotation angle with an error that is lower than 0.5%. Some experiments have also been carried out on images of real eyes showing promising behavior of the system in the presence of blood or surgical instruments during real eye surgery. © 2014 Elsevier Ltd. All rights reserved.Monserrat Aranda, C.; Rupérez Moreno, MJ.; Alcañiz Raya, ML.; Mataix, J. (2014). Markerless monocular tracking system for guided external eye surgery. Computerized Medical Imaging and Graphics. 38(8):785-792. doi:10.1016/j.compmedimag.2014.08.001S78579238

Image features for visual teach-and-repeat navigation in changing environments

We present an evaluation of standard image features in the context of long-term visual teach-and-repeat navigation of mobile robots, where the environment exhibits significant changes in appearance caused by seasonal weather variations and daily illumination changes. We argue that for long-term autonomous navigation, the viewpoint-, scale- and rotation- invariance of the standard feature extractors is less important than their robustness to the mid- and long-term environment appearance changes. Therefore, we focus our evaluation on the robustness of image registration to variable lighting and naturally-occurring seasonal changes. We combine detection and description components of different image extractors and evaluate their performance on five datasets collected by mobile vehicles in three different outdoor environments over the course of one year. Moreover, we propose a trainable feature descriptor based on a combination of evolutionary algorithms and Binary Robust Independent Elementary Features, which we call GRIEF (Generated BRIEF). In terms of robustness to seasonal changes, the most promising results were achieved by the SpG/CNN and the STAR/GRIEF feature, which was slightly less robust, but faster to calculate

Molecular genetic identification of skeletal remains from the Second World War Konfin I mass grave in Slovenia

This paper describes molecular genetic identification of one third of the skeletal remains of 88 victims of postwar (June 1945) killings found in the Konfin I mass grave in Slovenia. Living relatives were traced for 36 victims. We analyzed 84 right femurs and compared their genetic profiles to the genetic material of living relatives. We cleaned the bones, removed surface contamination, and ground the bones into powder. Prior to DNA isolation using Biorobot EZ1 (Qiagen), the powder was decalcified. The nuclear DNA of the samples was quantified using the real-time polymerase chain reaction method. We extracted 0.8 to 100 ng DNA/g of bone powder from 82 bones. Autosomal genetic profiles and Y-chromosome haplotypes were obtained from 98% of the bones, and mitochondrial DNA (mtDNA) haplotypes from 95% of the bones for the HVI region and from 98% of the bones for the HVII region. Genetic profiles of the nuclear and mtDNA were determined for reference persons. For traceability in the event of contamination, we created an elimination database including genetic profiles of the nuclear and mtDNA of all persons that had been in contact with the skeletal remains. When comparing genetic profiles, we matched 28 of the 84 bones analyzed with living relatives (brothers, sisters, sons, daughters, nephews, or cousins). The statistical analyses showed a high confidence of correct identification for all 28 victims in the Konfin I mass grave (posterior probability ranged from 99.9% to more than 99.999999%)

Incremental spectral clustering and seasons: Appearance-based localization in outdoor environments

Abstract — The problem of appearance-based mapping and navigation in outdoor environments is far from trivial. In this paper, an appearance-based topological map, covering a large, mixed indoor and outdoor environment, is built incrementally by using panoramic images. The map is based on image similarity, so that the resulting segmentation of the world corresponds closely to the human concept of a place. Using high-resolution images and the epipolar constraint, the resulting map is shown to be very suitable for localization, even when the environment has undergone seasonal changes. I

Applied

Abstract — Local feature matching has become a commonly used method to compare images. For mobile robots, a reliable method for comparing images can constitute a key component for localization and loop closing tasks. In this paper, we address the issues of outdoor appearance-based topological localization for a mobile robot over time. Our data sets, each consisting of a large number of panoramic images, have been acquired over a period of nine months with large seasonal changes (snowcovered ground, bare trees, autumn leaves, dense foliage, etc.). Two different types of image feature algorithms, SIFT and the more recent SURF, have been used to compare the images. We show that two variants of SURF, called U-SURF and SURF-128, outperform the other algorithms in terms of accuracy and speed