The rolling shutter camera has received great attention due to its low cost imaging capability, however, the estimation of relative pose between rolling shutter cameras still remains a difficult problem owing to its line-by-line image capturing characteristics. To alleviate this problem, we exploit gyroscope measurements, angular velocity, along with image measurement to compute the relative pose between rolling shutter cameras. The gyroscope measurements provide the information about instantaneous motion that causes the rolling shutter distortion. Having gyroscope measurements in one hand, we simplify the relative pose estimation problem and find a minimal solution for the problem based on the Grobner basis polynomial solver. The proposed method requires only five points to compute relative pose between rolling shutter cameras, whereas previous methods require 20 or 44 corresponding points for linear and uniform rolling shutter geometry models, respectively. Experimental results on synthetic and real data verify the superiority of the proposed method over existing relative pose estimation methods.
On the other hand, the methods to utilize an inertial measurement unit (IMU) to deal with the rolling shutter distortion in visual odometry (VO) and SLAM have been also studied. Jia and Evans @cite_20 proposed a method to estimate the camera orientation using gyroscope measurements and to correct the rolling shutter distortion of the image. Guo al @cite_23 applied a rolling shutter camera projection model to the visual-inertial odometry framework that uses IMUs and cameras to estimate egomotion. They estimated the readout time of the rolling shutter camera as well as the time delay between the IMU and the camera. Albl al @cite_1 proposed a method to improve the speed and accuracy of the method in @cite_36 using the gravity measurements obtained from the IMU. In addition, IMUs are also used to solve conventional relative or absolute pose estimation problems for global shutter cameras. It has been studied to estimate relative pose with the partially known orientation angle between two cameras @cite_22 @cite_24 , or with known vertical direction @cite_15 @cite_6 .
@cite_22 @cite_36 @cite_1 @cite_6 @cite_24 @cite_23 @cite_15 @cite_20
1987441863 2892147056 2895748257 2411412724
All existing methods for vision-aided inertial navigation assume a camera with a global shutter, in which all the pixels in an image are captured simultaneously. However, the vast majority of consumer-grade cameras use rolling-shutter sensors, which capture each row of pixels at a slightly different time instant. The effects of the rolling shutter distortion when a camera is in motion can be very significant, and are not modelled by existing visual-inertial motion-tracking methods. In this paper we describe the first, to the best of our knowledge, method for vision-aided inertial navigation using rolling-shutter cameras. Specifically, we present an extended Kalman filter (EKF)-based method for visual-inertial odometry, which fuses the IMU measurements with observations of visual feature tracks provided by the camera. The key contribution of this work is a computationally tractable approach for taking into account the rolling-shutter effect, incurring only minimal approximations. The experimental results from the application of the method show that it is able to track, in real time, the position of a mobile phone moving in an unknown environment with an error accumulation of approximately 0.8 of the distance travelled, over hundreds of meters. In this work, we develop a novel dense planar-inertial SLAM (DPI-SLAM) system to reconstruct dense 3D models of large indoor environments using a hand-held RGB-D sensor and an inertial measurement unit (IMU). The preinte-grated IMU measurements are loosely-coupled with the dense visual odometry (VO) estimation and tightly-coupled with the planar measurements in a full SLAM framework. The poses, velocities, and IMU biases are optimized together with the planar landmarks in a global factor graph using incremental smoothing and mapping with the Bayes Tree (iSAM2). With odometry estimation using both RGB-D and IMU data, our system can keep track of the poses of the sensors even without sufficient planes or visual information (e.g. textureless walls) temporarily. Modeling planes and IMU states in the fully probabilistic global optimization reduces the drift that distorts the reconstruction results of other SLAM algorithms. Moreover, structural constraints between nearby planes (e.g. right angles) are added into the DPI-SLAM system, which further recovers the drift and distortion. We test our DPI-SLAM on large indoor datasets and demonstrate its state-of-the-art performance as the first planar-inertial SLAM system. In this work, we propose a method that combines a single hand-held camera and a set of Inertial Measurement Units (IMUs) attached at the body limbs to estimate accurate 3D poses in the wild. This poses many new challenges: the moving camera, heading drift, cluttered background, occlusions and many people visible in the video. We associate 2D pose detections in each image to the corresponding IMU-equipped persons by solving a novel graph based optimization problem that forces 3D to 2D coherency within a frame and across long range frames. Given associations, we jointly optimize the pose of a statistical body model, the camera pose and heading drift using a continuous optimization framework. We validated our method on the TotalCapture dataset, which provides video and IMU synchronized with ground truth. We obtain an accuracy of 26 mm, which makes it accurate enough to serve as a benchmark for image-based 3D pose estimation in the wild. Using our method, we recorded 3D Poses in the Wild (3DPW), a new dataset consisting of more than 51, 000 frames with accurate 3D pose in challenging sequences, including walking in the city, going up-stairs, having coffee or taking the bus. We make the reconstructed 3D poses, video, IMU and 3D models available for research purposes at http: virtualhumans.mpi-inf.mpg.de 3DPW. We propose a novel direct visual-inertial odometry method for stereo cameras. Camera pose, velocity and IMU biases are simultaneously estimated by minimizing a combined photometric and inertial energy functional. This allows us to exploit the complementary nature of vision and inertial data. At the same time, and in contrast to all existing visual-inertial methods, our approach is fully direct: geometry is estimated in the form of semi-dense depth maps instead of manually designed sparse keypoints. Depth information is obtained both from static stereo - relating the fixed-baseline images of the stereo camera - and temporal stereo - relating images from the same camera, taken at different points in time. We show that our method outperforms not only vision-only or loosely coupled approaches, but also can achieve more accurate results than state-of-the-art keypoint-based methods on different datasets, including rapid motion and significant illumination changes. In addition, our method provides high-fidelity semi-dense, metric reconstructions of the environment, and runs in real-time on a CPU.