GSLAM: Initialization-robust Monocular Visual SLAM via Global Structure-from-Motion

Many monocular visual SLAM algorithms are derived from incremental structure-from-motion (SfM) methods. This work proposes a novel monocular SLAM method which integrates recent advances made in global SfM. In particular, we present two main contributions to visual SLAM. First, we solve the visual odometry problem by a novel rank-1 matrix factorization technique which is more robust to the errors in map initialization. Second, we adopt a recent global SfM method for the pose-graph optimization, which leads to a multi-stage linear formulation and enables L1 optimization for better robustness to false loops. The combination of these two approaches generates more robust reconstruction and is significantly faster (4X) than recent state-of-the-art SLAM systems. We also present a new dataset recorded with ground truth camera motion in a Vicon motion capture room, and compare our method to prior systems on it and established benchmark datasets.Comment: 3DV 2017 Project Page: https://frobelbest.github.io/gsla

3D MODELING OF THE MICHIGAN TECH HUSKY STATUE USING A CLOSE-RANGE PHOTOGRAMMETRIC APPROACH

In fall of 2014 a three meter tall statue of a Husky was erected on the campus of Michigan Technological University. The Husky is Michigan Tech’s mascot and a symbol of the snowy frozen north woods in which the campus is located. The statue was conceived and funded by the universities alumni association and by donors who paid to have bricks engraved around the statue. A team of graduate students in the Integrated Geospatial Technology program came up with the idea of using photogrammetry to model the statue in order to perform accurate measurements of area and volume. This initial idea was taken another step by the need for a course project in close-range photogrammetry and a desire by the alumni association to publish a 3D model of the statue online. This study tests two software packages that can be used to create a photogrammetric model of the statue. A final data set has yet to be collected; however initial attempts have been successful in creating a highly detailed digital model. With the weather clearing and the snow melting work will continue on this project

Omnidirectional DSO: Direct Sparse Odometry with Fisheye Cameras

We propose a novel real-time direct monocular visual odometry for omnidirectional cameras. Our method extends direct sparse odometry (DSO) by using the unified omnidirectional model as a projection function, which can be applied to fisheye cameras with a field-of-view (FoV) well above 180 degrees. This formulation allows for using the full area of the input image even with strong distortion, while most existing visual odometry methods can only use a rectified and cropped part of it. Model parameters within an active keyframe window are jointly optimized, including the intrinsic/extrinsic camera parameters, 3D position of points, and affine brightness parameters. Thanks to the wide FoV, image overlap between frames becomes bigger and points are more spatially distributed. Our results demonstrate that our method provides increased accuracy and robustness over state-of-the-art visual odometry algorithms.Comment: Accepted by IEEE Robotics and Automation Letters (RA-L), 2018 and IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 201

KALMANSAC: robust filtering by consensus

We propose an algorithm to perform causal inference of the state of a dynamical model when the measurements are corrupted by outliers. While the optimal (maximum-likelihood) solution has doubly exponential complexity due to the combinatorial explosion of possible choices of inliers, we exploit the structure of the problem to design a sampling-based algorithm that has constant complexity. We derive our algorithm from the equations of the optimal filter, which makes our approximation explicit. Our work is motivated by real-time tracking and the estimation of structure from motion (SFM). We test our algorithm for on-line outlier rejection both for tracking and for SFM. We show that our approach can tolerate a large proportion of outliers, whereas previous causal robust statistical inference methods failed with less than half as many. Our work can be thought of as the extension of random sample consensus algorithms to dynamic data, or as the implementation of pseudo-Bayesian filtering algorithms in a sampling framework. © 2005 IEEE