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

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

Towards High-Frequency Tracking and Fast Edge-Aware Optimization

This dissertation advances the state of the art for AR/VR tracking systems by increasing the tracking frequency by orders of magnitude and proposes an efficient algorithm for the problem of edge-aware optimization. AR/VR is a natural way of interacting with computers, where the physical and digital worlds coexist. We are on the cusp of a radical change in how humans perform and interact with computing. Humans are sensitive to small misalignments between the real and the virtual world, and tracking at kilo-Hertz frequencies becomes essential. Current vision-based systems fall short, as their tracking frequency is implicitly limited by the frame-rate of the camera. This thesis presents a prototype system which can track at orders of magnitude higher than the state-of-the-art methods using multiple commodity cameras. The proposed system exploits characteristics of the camera traditionally considered as flaws, namely rolling shutter and radial distortion. The experimental evaluation shows the effectiveness of the method for various degrees of motion. Furthermore, edge-aware optimization is an indispensable tool in the computer vision arsenal for accurate filtering of depth-data and image-based rendering, which is increasingly being used for content creation and geometry processing for AR/VR. As applications increasingly demand higher resolution and speed, there exists a need to develop methods that scale accordingly. This dissertation proposes such an edge-aware optimization framework which is efficient, accurate, and algorithmically scales well, all of which are much desirable traits not found jointly in the state of the art. The experiments show the effectiveness of the framework in a multitude of computer vision tasks such as computational photography and stereo.Comment: PhD thesi