11,316 research outputs found
Linear Global Translation Estimation with Feature Tracks
This paper derives a novel linear position constraint for cameras seeing a
common scene point, which leads to a direct linear method for global camera
translation estimation. Unlike previous solutions, this method deals with
collinear camera motion and weak image association at the same time. The final
linear formulation does not involve the coordinates of scene points, which
makes it efficient even for large scale data. We solve the linear equation
based on norm, which makes our system more robust to outliers in
essential matrices and feature correspondences. We experiment this method on
both sequentially captured images and unordered Internet images. The
experiments demonstrate its strength in robustness, accuracy, and efficiency.Comment: Changes: 1. Adopt BMVC2015 style; 2. Combine sections 3 and 5; 3.
Move "Evaluation on synthetic data" out to supplementary file; 4. Divide
subsection "Evaluation on general data" to subsections "Experiment on
sequential data" and "Experiment on unordered Internet data"; 5. Change Fig.
1 and Fig.8; 6. Move Fig. 6 and Fig. 7 to supplementary file; 7 Change some
symbols; 8. Correct some typo
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
Ultimate SLAM? Combining Events, Images, and IMU for Robust Visual SLAM in HDR and High Speed Scenarios
Event cameras are bio-inspired vision sensors that output pixel-level
brightness changes instead of standard intensity frames. These cameras do not
suffer from motion blur and have a very high dynamic range, which enables them
to provide reliable visual information during high speed motions or in scenes
characterized by high dynamic range. However, event cameras output only little
information when the amount of motion is limited, such as in the case of almost
still motion. Conversely, standard cameras provide instant and rich information
about the environment most of the time (in low-speed and good lighting
scenarios), but they fail severely in case of fast motions, or difficult
lighting such as high dynamic range or low light scenes. In this paper, we
present the first state estimation pipeline that leverages the complementary
advantages of these two sensors by fusing in a tightly-coupled manner events,
standard frames, and inertial measurements. We show on the publicly available
Event Camera Dataset that our hybrid pipeline leads to an accuracy improvement
of 130% over event-only pipelines, and 85% over standard-frames-only
visual-inertial systems, while still being computationally tractable.
Furthermore, we use our pipeline to demonstrate - to the best of our knowledge
- the first autonomous quadrotor flight using an event camera for state
estimation, unlocking flight scenarios that were not reachable with traditional
visual-inertial odometry, such as low-light environments and high-dynamic range
scenes.Comment: 8 pages, 9 figures, 2 table
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