Local Feature Selection and Global Energy Optimization in Stereo

The human brain can fuse two slightly different views from left and right eyes and perceive depth. This process of stereopsis entails identifying matching locations in the two images and recovering the depth from their disparity. This can be done only approximately: ambiguity arising from such factors as noise, periodicity, and large regions of constan

Omnidirectional Vision Based Topological Navigation

Goedemé T., Van Gool L., ''Omnidirectional vision based topological navigation'', Mobile robots navigation, pp. 172-196, Barrera Alejandra, ed., March 2010, InTech.status: publishe

Fundamental matrix estimation without prior match

ISBN: 1-4244-1437-7 ISSN: 1522-4880International audienceThis paper presents a probabilistic framework for computing correspondences and fundamental matrix in the structure from motion problem. Inspired by Moisan and Stival, we suggest using an a contrario model, which is a good answer to threshold problems in the robust filtering context. Contrary to most existing algorithms where perceptual correspondence setting and geometry evaluation are independent steps, the proposed algorithm is an all-in-one approach. We show that it is robust to repeated patterns which are usually difficult to unambiguously match and thus raise many problems in the fundamental matrix estimation

Structure-from-motion in Spherical Video using the von Mises-Fisher Distribution

In this paper, we present a complete pipeline for computing structure-from-motion from the sequences of spherical images. We revisit problems from multiview geometry in the context of spherical images. In particular, we propose methods suited to spherical camera geometry for the spherical-n-point problem (estimating camera pose for a spherical image) and calibrated spherical reconstruction (estimating the position of a 3-D point from multiple spherical images). We introduce a new probabilistic interpretation of spherical structure-from-motion which uses the von Mises-Fisher distribution to model noise in spherical feature point positions. This model provides an alternate objective function that we use in bundle adjustment. We evaluate our methods quantitatively and qualitatively on both synthetic and real world data and show that our methods developed for spherical images outperform straightforward adaptations of methods developed for perspective images. As an application of our method, we use the structure-from-motion output to stabilise the viewing direction in fully spherical video

Keyframe-based monocular SLAM: design, survey, and future directions

Extensive research in the field of monocular SLAM for the past fifteen years has yielded workable systems that found their way into various applications in robotics and augmented reality. Although filter-based monocular SLAM systems were common at some time, the more efficient keyframe-based solutions are becoming the de facto methodology for building a monocular SLAM system. The objective of this paper is threefold: first, the paper serves as a guideline for people seeking to design their own monocular SLAM according to specific environmental constraints. Second, it presents a survey that covers the various keyframe-based monocular SLAM systems in the literature, detailing the components of their implementation, and critically assessing the specific strategies made in each proposed solution. Third, the paper provides insight into the direction of future research in this field, to address the major limitations still facing monocular SLAM; namely, in the issues of illumination changes, initialization, highly dynamic motion, poorly textured scenes, repetitive textures, map maintenance, and failure recovery

Determining point correspondences between two views under geometric constraint and photometric consistency

Matching or tracking points of interest between several views is one of the keystones of many computer vision applications, especially when considering structure and motion estimation. The procedure generally consists in several independent steps, basically 1) point of interest extraction, 2) point of interest matching by keeping only the ``best correspondences'' with respect to similarity between some local descriptors, 3) correspondence pruning to keep those consistent with an estimated camera motion (here, consistent with epipolar constraints or homography transformation). Each step in itself is a touchy task which may endanger the whole process. In particular, repeated patterns give lots of false matches in step 2) which are hardly, if never, recovered by step 3). Starting from a statistical model by Moisan and Stival, we propose a new one-stage approach to steps 2) and 3), which does not need tricky parameters. The advantage of the proposed method is its robustness to repeated patterns