33 research outputs found
Minimal Solvers for Monocular Rolling Shutter Compensation under Ackermann Motion
Modern automotive vehicles are often equipped with a budget commercial
rolling shutter camera. These devices often produce distorted images due to the
inter-row delay of the camera while capturing the image. Recent methods for
monocular rolling shutter motion compensation utilize blur kernel and the
straightness property of line segments. However, these methods are limited to
handling rotational motion and also are not fast enough to operate in real
time. In this paper, we propose a minimal solver for the rolling shutter motion
compensation which assumes known vertical direction of the camera. Thanks to
the Ackermann motion model of vehicles which consists of only two motion
parameters, and two parameters for the simplified depth assumption that lead to
a 4-line algorithm. The proposed minimal solver estimates the rolling shutter
camera motion efficiently and accurately. The extensive experiments on real and
simulated datasets demonstrate the benefits of our approach in terms of
qualitative and quantitative results.Comment: Submitted to WACV 201
Urban Environment Navigation with Real-Time Data Utilizing Computer Vision, Inertial, and GPS Sensors
The purpose of this research was to obtain a navigation solution that used real data, in a degraded or denied global positioning system (GPS) environment, from low cost commercial o the shelf sensors. The sensors that were integrated together were a commercial inertial measurement unit (IMU), monocular camera computer vision algorithm, and GPS. Furthermore, the monocular camera computer vision algorithm had to be robust enough to handle any camera orientation that was presented to it. This research develops a visual odometry 2-D zero velocity measurement that is derived by both the features points that are extracted from a monocular camera and the rotation values given by an IMU. By presenting measurements as a 2-D zero velocity measurements, errors associated with scale, which is unobservable by a monocular camera, can be removed from the measurements. The 2-D zero velocity measurements are represented as two normalized velocity vectors that are orthogonal to the vehicle\u27s direction of travel, and are used to determine the error in the INS\u27s measured velocity vector. This error is produced by knowing which directions the vehicle is not moving, given by the 2-D zero velocity measurements, in and comparing it to the direction of travel the vehicle is thought to be moving in. The performance was evaluated by comparing results that were obtained when different sensor pairings of a commercial IMU, GPS, and monocular computer vision algorithm were used to obtain the vehicle\u27s trajectory. Three separate monocular cameras, that each pointed in a different directions, were tested independently. Finally, the solutions provided by the GPS were degraded (i.e., the number of satellites available from the GPS were limited) to determine the e effectiveness of adding a monocular computer vision algorithm to a system operating with a degraded GPS solution
Faster than FAST: GPU-Accelerated Frontend for High-Speed VIO
The recent introduction of powerful embedded graphics processing units (GPUs)
has allowed for unforeseen improvements in real-time computer vision
applications. It has enabled algorithms to run onboard, well above the standard
video rates, yielding not only higher information processing capability, but
also reduced latency. This work focuses on the applicability of efficient
low-level, GPU hardware-specific instructions to improve on existing computer
vision algorithms in the field of visual-inertial odometry (VIO). While most
steps of a VIO pipeline work on visual features, they rely on image data for
detection and tracking, of which both steps are well suited for
parallelization. Especially non-maxima suppression and the subsequent feature
selection are prominent contributors to the overall image processing latency.
Our work first revisits the problem of non-maxima suppression for feature
detection specifically on GPUs, and proposes a solution that selects local
response maxima, imposes spatial feature distribution, and extracts features
simultaneously. Our second contribution introduces an enhanced FAST feature
detector that applies the aforementioned non-maxima suppression method.
Finally, we compare our method to other state-of-the-art CPU and GPU
implementations, where we always outperform all of them in feature tracking and
detection, resulting in over 1000fps throughput on an embedded Jetson TX2
platform. Additionally, we demonstrate our work integrated in a VIO pipeline
achieving a metric state estimation at ~200fps.Comment: IEEE International Conference on Intelligent Robots and Systems
(IROS), 2020. Open-source implementation available at
https://github.com/uzh-rpg/vili