1,977 research outputs found
Distributed scene reconstruction from multiple mobile platforms
Recent research on mobile robotics has produced new designs that provide
house-hold robots with omnidirectional motion. The image sensor embedded
in these devices motivates the application of 3D vision techniques on them
for navigation and mapping purposes. In addition to this, distributed cheapsensing
systems acting as unitary entity have recently been discovered as an
efficient alternative to expensive mobile equipment.
In this work we present an implementation of a visual reconstruction method,
structure from motion (SfM), on a low-budget, omnidirectional mobile platform,
and extend this method to distributed 3D scene reconstruction with
several instances of such a platform.
Our approach overcomes the challenges yielded by the plaform. The unprecedented
levels of noise produced by the image compression typical of
the platform is processed by our feature filtering methods, which ensure
suitable feature matching populations for epipolar geometry estimation by
means of a strict quality-based feature selection. The robust pose estimation
algorithms implemented, along with a novel feature tracking system,
enable our incremental SfM approach to novelly deal with ill-conditioned
inter-image configurations provoked by the omnidirectional motion. The
feature tracking system developed efficiently manages the feature scarcity
produced by noise and outputs quality feature tracks, which allow robust
3D mapping of a given scene even if - due to noise - their length is shorter
than what it is usually assumed for performing stable 3D reconstructions.
The distributed reconstruction from multiple instances of SfM is attained
by applying loop-closing techniques. Our multiple reconstruction system
merges individual 3D structures and resolves the global scale problem with
minimal overlaps, whereas in the literature 3D mapping is obtained by overlapping
stretches of sequences. The performance of this system is demonstrated
in the 2-session case.
The management of noise, the stability against ill-configurations and the
robustness of our SfM system is validated on a number of experiments and
compared with state-of-the-art approaches. Possible future research areas
are also discussed
Space and camera path reconstruction for omni-directional vision
In this paper, we address the inverse problem of reconstructing a scene as
well as the camera motion from the image sequence taken by an omni-directional
camera. Our structure from motion results give sharp conditions under which the
reconstruction is unique. For example, if there are three points in general
position and three omni-directional cameras in general position, a unique
reconstruction is possible up to a similarity. We then look at the
reconstruction problem with m cameras and n points, where n and m can be large
and the over-determined system is solved by least square methods. The
reconstruction is robust and generalizes to the case of a dynamic environment
where landmarks can move during the movie capture. Possible applications of the
result are computer assisted scene reconstruction, 3D scanning, autonomous
robot navigation, medical tomography and city reconstructions
Under vehicle perception for high level safety measures using a catadioptric camera system
In recent years, under vehicle surveillance and the classification of the vehicles become an indispensable task that must be achieved for security measures in certain areas such as shopping centers, government buildings, army camps etc. The main challenge to achieve this task is to monitor the under
frames of the means of transportations. In this paper, we present a novel solution to achieve this aim. Our solution consists of three main parts: monitoring, detection and classification. In the first part we design a new catadioptric camera system in which the perspective camera points downwards to the catadioptric mirror mounted to the body of a mobile robot. Thanks to the
catadioptric mirror the scenes against the camera optical axis direction can be viewed. In the second part we use speeded up robust features (SURF) in an object recognition algorithm. Fast appearance based mapping algorithm (FAB-MAP) is exploited for the classification of the means of transportations in the third
part. Proposed technique is implemented in a laboratory environment
Calibration by correlation using metric embedding from non-metric similarities
This paper presents a new intrinsic calibration method that allows us to calibrate a generic single-view point camera just
by waving it around. From the video sequence obtained while the camera undergoes random motion, we compute the pairwise time
correlation of the luminance signal for a subset of the pixels. We show that, if the camera undergoes a random uniform motion, then
the pairwise correlation of any pixels pair is a function of the distance between the pixel directions on the visual sphere. This leads to
formalizing calibration as a problem of metric embedding from non-metric measurements: we want to find the disposition of pixels on
the visual sphere from similarities that are an unknown function of the distances. This problem is a generalization of multidimensional
scaling (MDS) that has so far resisted a comprehensive observability analysis (can we reconstruct a metrically accurate embedding?)
and a solid generic solution (how to do so?). We show that the observability depends both on the local geometric properties (curvature)
as well as on the global topological properties (connectedness) of the target manifold. We show that, in contrast to the Euclidean case,
on the sphere we can recover the scale of the points distribution, therefore obtaining a metrically accurate solution from non-metric
measurements. We describe an algorithm that is robust across manifolds and can recover a metrically accurate solution when the metric
information is observable. We demonstrate the performance of the algorithm for several cameras (pin-hole, fish-eye, omnidirectional),
and we obtain results comparable to calibration using classical methods. Additional synthetic benchmarks show that the algorithm
performs as theoretically predicted for all corner cases of the observability analysis
Occlusion Handling using Semantic Segmentation and Visibility-Based Rendering for Mixed Reality
Real-time occlusion handling is a major problem in outdoor mixed reality
system because it requires great computational cost mainly due to the
complexity of the scene. Using only segmentation, it is difficult to accurately
render a virtual object occluded by complex objects such as trees, bushes etc.
In this paper, we propose a novel occlusion handling method for real-time,
outdoor, and omni-directional mixed reality system using only the information
from a monocular image sequence. We first present a semantic segmentation
scheme for predicting the amount of visibility for different type of objects in
the scene. We also simultaneously calculate a foreground probability map using
depth estimation derived from optical flow. Finally, we combine the
segmentation result and the probability map to render the computer generated
object and the real scene using a visibility-based rendering method. Our
results show great improvement in handling occlusions compared to existing
blending based methods
Improved 3D sparse maps for high-performance SFM with low-cost omnidirectional robots
We consider the use of low-budget omnidirectional platforms for 3D mapping and self-localisation. These robots specifically permit rotational motion in the plane around a central axis, with negligible displacement. In addition, low resolution and compressed imagery, typical of the platform used, results in high level of image noise (_ ✠10). We observe highly sparse image feature matches over narrow inter-image baselines. This particular configuration poses a challenge for epipolar geometry extraction and accurate 3D point triangulation, upon which a standard structure from motion formulation is based. We propose a novel technique for both feature filtering and tracking that solves these problems, via a novel approach to the management of feature bundles. Noisy matches are efficiently trimmed, and the scarcity of the remaining image features is adequately overcome, generating densely populated maps of highly accurate and robust 3D image features. The effectiveness of the approach is demonstrated under a variety of scenarios in experiments conducted with low-budget commercial robots
An adaptive spherical view representation for navigation in changing environments
Real-world environments such as houses and offices change over time, meaning that a mobile robotâs map will become out of date. In previous work we introduced a method to update the reference views in a topological map so that a mobile robot could continue to localize itself in a changing environment using omni-directional vision. In this work we extend this longterm updating mechanism to incorporate a spherical metric representation of the observed visual features for each node in the topological map. Using multi-view geometry we are then able to estimate the heading of the robot, in order to enable navigation between the nodes of the map, and to simultaneously adapt the spherical view representation in response to environmental changes. The results demonstrate the persistent performance of the proposed system in a long-term experiment
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