7,171 research outputs found
Accurate and linear time pose estimation from points and lines
The final publication is available at link.springer.comThe Perspective-n-Point (PnP) problem seeks to estimate the pose of a calibrated camera from n 3Dto-2D point correspondences. There are situations, though, where PnP solutions are prone to fail because feature point correspondences cannot be reliably estimated (e.g. scenes with repetitive patterns or with low texture). In such
scenarios, one can still exploit alternative geometric entities, such as lines, yielding the so-called Perspective-n-Line (PnL) algorithms. Unfortunately, existing PnL solutions are not as accurate and efficient as their point-based
counterparts. In this paper we propose a novel approach to introduce 3D-to-2D line correspondences into a PnP formulation, allowing to simultaneously process points and lines. For this purpose we introduce an algebraic line error
that can be formulated as linear constraints on the line endpoints, even when these are not directly observable. These constraints can then be naturally integrated within the linear formulations of two state-of-the-art point-based algorithms,
the OPnP and the EPnP, allowing them to indistinctly handle points, lines, or a combination of them. Exhaustive experiments show that the proposed formulation brings remarkable boost in performance compared to only point or
only line based solutions, with a negligible computational overhead compared to the original OPnP and EPnP.Peer ReviewedPostprint (author's final draft
SPLODE: Semi-Probabilistic Point and Line Odometry with Depth Estimation from RGB-D Camera Motion
Active depth cameras suffer from several limitations, which cause incomplete
and noisy depth maps, and may consequently affect the performance of RGB-D
Odometry. To address this issue, this paper presents a visual odometry method
based on point and line features that leverages both measurements from a depth
sensor and depth estimates from camera motion. Depth estimates are generated
continuously by a probabilistic depth estimation framework for both types of
features to compensate for the lack of depth measurements and inaccurate
feature depth associations. The framework models explicitly the uncertainty of
triangulating depth from both point and line observations to validate and
obtain precise estimates. Furthermore, depth measurements are exploited by
propagating them through a depth map registration module and using a
frame-to-frame motion estimation method that considers 3D-to-2D and 2D-to-3D
reprojection errors, independently. Results on RGB-D sequences captured on
large indoor and outdoor scenes, where depth sensor limitations are critical,
show that the combination of depth measurements and estimates through our
approach is able to overcome the absence and inaccuracy of depth measurements.Comment: IROS 201
Graph matching with a dual-step EM algorithm
This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data
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
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