1,132 research outputs found
Rolling-Shutter Modelling for Direct Visual-Inertial Odometry
We present a direct visual-inertial odometry (VIO) method which estimates the
motion of the sensor setup and sparse 3D geometry of the environment based on
measurements from a rolling-shutter camera and an inertial measurement unit
(IMU).
The visual part of the system performs a photometric bundle adjustment on a
sparse set of points. This direct approach does not extract feature points and
is able to track not only corners, but any pixels with sufficient gradient
magnitude. Neglecting rolling-shutter effects in the visual part severely
degrades accuracy and robustness of the system. In this paper, we incorporate a
rolling-shutter model into the photometric bundle adjustment that estimates a
set of recent keyframe poses and the inverse depth of a sparse set of points.
IMU information is accumulated between several frames using measurement
preintegration, and is inserted into the optimization as an additional
constraint between selected keyframes. For every keyframe we estimate not only
the pose but also velocity and biases to correct the IMU measurements. Unlike
systems with global-shutter cameras, we use both IMU measurements and
rolling-shutter effects of the camera to estimate velocity and biases for every
state.
Last, we evaluate our system on a novel dataset that contains global-shutter
and rolling-shutter images, IMU data and ground-truth poses for ten different
sequences, which we make publicly available. Evaluation shows that the proposed
method outperforms a system where rolling shutter is not modelled and achieves
similar accuracy to the global-shutter method on global-shutter data
Direct Sparse Odometry with Rolling Shutter
Neglecting the effects of rolling-shutter cameras for visual odometry (VO)
severely degrades accuracy and robustness. In this paper, we propose a novel
direct monocular VO method that incorporates a rolling-shutter model. Our
approach extends direct sparse odometry which performs direct bundle adjustment
of a set of recent keyframe poses and the depths of a sparse set of image
points. We estimate the velocity at each keyframe and impose a
constant-velocity prior for the optimization. In this way, we obtain a near
real-time, accurate direct VO method. Our approach achieves improved results on
challenging rolling-shutter sequences over state-of-the-art global-shutter VO
Revisiting Rolling Shutter Bundle Adjustment: Toward Accurate and Fast Solution
We propose a robust and fast bundle adjustment solution that estimates the
6-DoF pose of the camera and the geometry of the environment based on
measurements from a rolling shutter (RS) camera. This tackles the challenges in
the existing works, namely relying on additional sensors, high frame rate video
as input, restrictive assumptions on camera motion, readout direction, and poor
efficiency. To this end, we first investigate the influence of normalization to
the image point on RSBA performance and show its better approximation in
modelling the real 6-DoF camera motion. Then we present a novel analytical
model for the visual residual covariance, which can be used to standardize the
reprojection error during the optimization, consequently improving the overall
accuracy. More importantly, the combination of normalization and covariance
standardization weighting in RSBA (NW-RSBA) can avoid common planar degeneracy
without needing to constrain the filming manner. Besides, we propose an
acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix
and Schur complement. The extensive synthetic and real data experiments verify
the effectiveness and efficiency of the proposed solution over the
state-of-the-art works. We also demonstrate the proposed method can be easily
implemented and plug-in famous GSSfM and GSSLAM systems as completed RSSfM and
RSSLAM solutions
Joint 3D Shape and Motion Estimation from Rolling Shutter Light-Field Images
In this paper, we propose an approach to address the problem of 3D
reconstruction of scenes from a single image captured by a light-field camera
equipped with a rolling shutter sensor. Our method leverages the 3D information
cues present in the light-field and the motion information provided by the
rolling shutter effect. We present a generic model for the imaging process of
this sensor and a two-stage algorithm that minimizes the re-projection error
while considering the position and motion of the camera in a motion-shape
bundle adjustment estimation strategy. Thereby, we provide an instantaneous 3D
shape-and-pose-and-velocity sensing paradigm. To the best of our knowledge,
this is the first study to leverage this type of sensor for this purpose. We
also present a new benchmark dataset composed of different light-fields showing
rolling shutter effects, which can be used as a common base to improve the
evaluation and tracking the progress in the field. We demonstrate the
effectiveness and advantages of our approach through several experiments
conducted for different scenes and types of motions. The source code and
dataset are publicly available at: https://github.com/ICB-Vision-AI/RSL
Street View Motion-from-Structure-from-Motion
We describe a structure-from-motion framework that handles “generalized ” cameras, such as moving rolling-shutter cameras, and works at an unprecedented scale— billions of images covering millions of linear kilometers of roads—by exploiting a good relative pose prior along vehicle paths. We exhibit a planet-scale, appearance-augmented point cloud constructed with our framework and demonstrate its practical use in correcting the pose of a street-level image collection. 1
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