1,308 research outputs found
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
H-SLAM: Hybrid Direct-Indirect Visual SLAM
The recent success of hybrid methods in monocular odometry has led to many
attempts to generalize the performance gains to hybrid monocular SLAM. However,
most attempts fall short in several respects, with the most prominent issue
being the need for two different map representations (local and global maps),
with each requiring different, computationally expensive, and often redundant
processes to maintain. Moreover, these maps tend to drift with respect to each
other, resulting in contradicting pose and scene estimates, and leading to
catastrophic failure. In this paper, we propose a novel approach that makes use
of descriptor sharing to generate a single inverse depth scene representation.
This representation can be used locally, queried globally to perform loop
closure, and has the ability to re-activate previously observed map points
after redundant points are marginalized from the local map, eliminating the
need for separate and redundant map maintenance processes. The maps generated
by our method exhibit no drift between each other, and can be computed at a
fraction of the computational cost and memory footprint required by other
monocular SLAM systems. Despite the reduced resource requirements, the proposed
approach maintains its robustness and accuracy, delivering performance
comparable to state-of-the-art SLAM methods (e.g., LDSO, ORB-SLAM3) on the
majority of sequences from well-known datasets like EuRoC, KITTI, and TUM VI.
The source code is available at: https://github.com/AUBVRL/fslam_ros_docker
Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation
This paper presents a nonlinear image registration algorithm based on the setting of Large Deformation Diffeomorphic Metric Mapping (LDDMM). but with a more efficient optimisation scheme - both in terms of memory required and the number of iterations required to reach convergence. Rather than perform a variational optimisation on a series of velocity fields, the algorithm is formulated to use a geodesic shooting procedure, so that only an initial velocity is estimated. A Gauss-Newton optimisation strategy is used to achieve faster convergence. The algorithm was evaluated using freely available manually labelled datasets, and found to compare favourably with other inter-subject registration algorithms evaluated using the same data. (C) 2011 Elsevier Inc. All rights reserved
Spatial Detection of Vehicles in Images using Convolutional Neural Networks and Stereo Matching
Convolutional Neural Networks combined with a state of the artstereo-matching method are used to find and estimate the 3D positionof vehicles in pairs of stereo images. Pixel positions of vehiclesare first estimated separately in pairs of stereo images usinga Convolutional Neural Network for regression. These coordinatesare then combined with a state-of-art stereo-matching method todetermine the depth, and thus the 3D location, of the vehicles. Weshow in this paper that cars can be detected with a combined accuracyof approximately 90% with a tolerated radius error of 5%,and a Mean Absolute Error of 5.25m on depth estimation for carsup to 50m away
IR Shape From Shading Enhanced RGBD for 3D Scanning
RGBD Cameras such as the Microsoft Kinect that can quickly provideusable depth maps have become very affordable, and thusvery popular and abundant in recent years. Beyond gaming, RGBDcameras can have numerous applications, including their use in affordable3D scanners. These cameras however are limited in theirability to capture finer details. We explore the use of additional3D reconstruction algorithms to enhance the depth maps producedfrom RGBD cameras, allowing them to capture more detail
Road Defect Detection in Street View Images using Texture Descriptors and Contour Maps
Road quality assessment is a crucial part in municipalities’ workto maintain their infrastructure, plan upgrades, and manage theirbudgets. Properly maintaining this infrastructure relies heavily onconsistently monitoring its condition and deterioration over time.This can be a challenge, especially in larger towns and cities wherethere is a lot of city property to keep an eye on. We review roadquality assessment methods currently employed, and then describeour novel algorithm aimed at identifying distressed road regionsfrom street view images and pinpointing cracks within them. Wepredict distressed regions by computing Fisher vectors on localSIFT descriptors and classifying them with an SVM trained to distinguishbetween road qualities. We follow this step with a comparisonto a weighed contour map within these distressed regionsto identify exact crack and defect locations, and use the contourweights to predict the crack severity. Promising results are obtainedon our manually annotated dataset, which indicate the viability ofusing this cost-effective system to perform road quality assessmentat a municipal level
Scaled Monocular Visual SLAM
The fundamental shortcoming underlying monocular-based localizationand mapping solutions (SfM, Visual SLAM) is the fact thatthe obtained maps and motion are solved up to an unknown scale.Yet, the literature provides interesting solutions to scale estimationusing cues from focus or defocus of a camera. In this paper, wetake advantage of the scale offered by image focus to properly initializeVisual SLAM with a correct metric scale. We provide experimentsshowing the success of the proposed method and discussits limitations
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