3,118 research outputs found
Hybrid Focal Stereo Networks for Pattern Analysis in Homogeneous Scenes
In this paper we address the problem of multiple camera calibration in the
presence of a homogeneous scene, and without the possibility of employing
calibration object based methods. The proposed solution exploits salient
features present in a larger field of view, but instead of employing active
vision we replace the cameras with stereo rigs featuring a long focal analysis
camera, as well as a short focal registration camera. Thus, we are able to
propose an accurate solution which does not require intrinsic variation models
as in the case of zooming cameras. Moreover, the availability of the two views
simultaneously in each rig allows for pose re-estimation between rigs as often
as necessary. The algorithm has been successfully validated in an indoor
setting, as well as on a difficult scene featuring a highly dense pilgrim crowd
in Makkah.Comment: 13 pages, 6 figures, submitted to Machine Vision and Application
3D Visual Perception for Self-Driving Cars using a Multi-Camera System: Calibration, Mapping, Localization, and Obstacle Detection
Cameras are a crucial exteroceptive sensor for self-driving cars as they are
low-cost and small, provide appearance information about the environment, and
work in various weather conditions. They can be used for multiple purposes such
as visual navigation and obstacle detection. We can use a surround multi-camera
system to cover the full 360-degree field-of-view around the car. In this way,
we avoid blind spots which can otherwise lead to accidents. To minimize the
number of cameras needed for surround perception, we utilize fisheye cameras.
Consequently, standard vision pipelines for 3D mapping, visual localization,
obstacle detection, etc. need to be adapted to take full advantage of the
availability of multiple cameras rather than treat each camera individually. In
addition, processing of fisheye images has to be supported. In this paper, we
describe the camera calibration and subsequent processing pipeline for
multi-fisheye-camera systems developed as part of the V-Charge project. This
project seeks to enable automated valet parking for self-driving cars. Our
pipeline is able to precisely calibrate multi-camera systems, build sparse 3D
maps for visual navigation, visually localize the car with respect to these
maps, generate accurate dense maps, as well as detect obstacles based on
real-time depth map extraction
Calibration and Sensitivity Analysis of a Stereo Vision-Based Driver Assistance System
Az http://intechweb.org/ alatti "Books" fĂŒl alatt kell rĂĄkeresni a "Stereo Vision" cĂmre Ă©s az 1. fejezetre
How to Train a CAT: Learning Canonical Appearance Transformations for Direct Visual Localization Under Illumination Change
Direct visual localization has recently enjoyed a resurgence in popularity
with the increasing availability of cheap mobile computing power. The
competitive accuracy and robustness of these algorithms compared to
state-of-the-art feature-based methods, as well as their natural ability to
yield dense maps, makes them an appealing choice for a variety of mobile
robotics applications. However, direct methods remain brittle in the face of
appearance change due to their underlying assumption of photometric
consistency, which is commonly violated in practice. In this paper, we propose
to mitigate this problem by training deep convolutional encoder-decoder models
to transform images of a scene such that they correspond to a previously-seen
canonical appearance. We validate our method in multiple environments and
illumination conditions using high-fidelity synthetic RGB-D datasets, and
integrate the trained models into a direct visual localization pipeline,
yielding improvements in visual odometry (VO) accuracy through time-varying
illumination conditions, as well as improved metric relocalization performance
under illumination change, where conventional methods normally fail. We further
provide a preliminary investigation of transfer learning from synthetic to real
environments in a localization context. An open-source implementation of our
method using PyTorch is available at https://github.com/utiasSTARS/cat-net.Comment: In IEEE Robotics and Automation Letters (RA-L) and presented at the
IEEE International Conference on Robotics and Automation (ICRA'18), Brisbane,
Australia, May 21-25, 201
From Calibration to Large-Scale Structure from Motion with Light Fields
Classic pinhole cameras project the multi-dimensional information of the light flowing through a scene onto a single 2D snapshot. This projection limits the information that can be reconstructed from the 2D acquisition. Plenoptic (or light field) cameras, on the other hand, capture a 4D slice of the plenoptic function, termed the âlight fieldâ. These cameras provide both spatial and angular information on the light flowing through a scene; multiple views are captured in a single photographic exposure facilitating various applications. This thesis is concerned with the modelling of light field (or plenoptic) cameras and the development of structure from motion pipelines using such cameras. Specifically, we develop a geometric model for a multi-focus plenoptic camera, followed by a complete pipeline for the calibration of the suggested model. Given a calibrated light field camera, we then remap the captured light field to a grid of pinhole images. We use these images to obtain metric 3D reconstruction through a novel framework for structure from motion with light fields. Finally, we suggest a linear and efficient approach for absolute pose estimation for light fields
Feature-based calibration of distributed smart stereo camera networks
A distributed smart camera network is a collective of vision-capable devices with enough processing power to execute algorithms for collaborative vision tasks. A true 3D sensing network applies to a broad range of applications, and local stereo vision capabilities at each node offer the potential for a particularly robust implementation. A novel spatial calibration method for such a network is presented, which obtains pose estimates suitable for collaborative 3D vision in a distributed fashion using two stages of registration on robust 3D features. The method is first described in a general, modular sense, assuming some ideal vision and registration algorithms. Then, existing algorithms are selected for a practical implementation. The method is designed independently of networking details, making only a few basic assumptions about the underlying network\u27s capabilities. Experiments using both software simulations and physical devices are designed and executed to demonstrate performance
Infrastructure-based Multi-Camera Calibration using Radial Projections
Multi-camera systems are an important sensor platform for intelligent systems
such as self-driving cars. Pattern-based calibration techniques can be used to
calibrate the intrinsics of the cameras individually. However, extrinsic
calibration of systems with little to no visual overlap between the cameras is
a challenge. Given the camera intrinsics, infrastucture-based calibration
techniques are able to estimate the extrinsics using 3D maps pre-built via SLAM
or Structure-from-Motion. In this paper, we propose to fully calibrate a
multi-camera system from scratch using an infrastructure-based approach.
Assuming that the distortion is mainly radial, we introduce a two-stage
approach. We first estimate the camera-rig extrinsics up to a single unknown
translation component per camera. Next, we solve for both the intrinsic
parameters and the missing translation components. Extensive experiments on
multiple indoor and outdoor scenes with multiple multi-camera systems show that
our calibration method achieves high accuracy and robustness. In particular,
our approach is more robust than the naive approach of first estimating
intrinsic parameters and pose per camera before refining the extrinsic
parameters of the system. The implementation is available at
https://github.com/youkely/InfrasCal.Comment: ECCV 202
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