With the spread of wearable devices and head mounted cameras, a wide range of application requiring precise user localization is now possible. In this paper we propose to treat the problem of obtaining the user position with respect to a known environment as a video registration problem. Video registration, i.e. the task of aligning an input video sequence to a pre-built 3D model, relies on a matching process of local keypoints extracted on the query sequence to a 3D point cloud. The overall registration performance is strictly tied to the actual quality of this 2D-3D matching, and can degrade if environmental conditions such as steep changes in lighting like the ones between day and night occur. To effectively register an egocentric video sequence under these conditions, we propose to tackle the source of the problem: the matching process. To overcome the shortcomings of standard matching techniques, we introduce a novel embedding space that allows us to obtain robust matches by jointly taking into account local descriptors, their spatial arrangement and their temporal robustness. The proposal is evaluated using unconstrained egocentric video sequences both in terms of matching quality and resulting registration performance using different 3D models of historical landmarks. The results show that the proposed method can outperform state of the art registration algorithms, in particular when dealing with the challenges of night and day sequences.
The task of registering a video sequence to a 3D model can be performed in two fundamentally different ways. The first, while not being a proper registration technique in the sense that the 3D model is not pre-built but learned online, is Simultaneous Localization and Mapping (SLAM) @cite_47 @cite_38 @cite_8 . SLAM jointly builds the 3D model of the scene and locates the camera in the environment using the 3D model as reference. Similarly to video registration, this process is often performed through robust keypoint matching; on the other hand a major difference is due to the fact that the camera employed in building the model and in acquiring the query frames is the same, so the internal camera parameters remain constant throughout the problem and both the model and query sequence share the same lighting and environmental conditions. Widely popular in the field of robotics, few SLAM approaches have been extended to the task of video registration.
@cite_38 @cite_47 @cite_8
2044124120 2187295369 2060977605 1906051104
In this paper we propose a robust and direct 2D-to-3D registration method for localizing 2D cameras in a known 3D environment. Although the 3D environment is known, localizing the cameras remains a challenging problem that is particularly undermined by the unknown 2D-3D correspondences, outliers, scale ambiguities and occlusions. Once the cameras are localized, the Structure-from-Motion reconstruction obtained from image correspondences is refined by means of a constrained nonlinear optimization that benefits from the knowledge of the scene. We also propose a common optimization framework for both localization and refinement steps in which projection errors in one view are minimized while preserving the existing relationships between images. The problem of occlusion and that of missing scene parts are handled by employing a scale histogram while the effect of data inaccuracies is minimized using an M-estimator-based technique. Image registration, and more generally scene registration, needs to be solved in mobile robotics for a number of tasks including localization, mapping, object recognition, visual odometry and loop-closure. This paper presents a flexible strategy to register scenes based on its planar structure, which can be used with different sensors that acquire 3D data like LIDAR, time-of-flight cameras, RGB-D sensors and stereo vision. The proposed strategy is based on the segmentation of the planar surfaces from the scene, and its representation using a graph which stores the geometric relationships between neighbouring planar patches. Uncertainty information from the planar patches is exploited in a hierarchical fashion to improve both the robustness and the efficiency of registration. Quick registration is achieved in indoor structured scenarios, offering advantages like a compact representation, and flexibility to adapt to different environments and sensors. Our method is validated with different sensors: a hand-held RGB-D camera and an omnidirectional RGB-D sensor; and for different applications: from visual-range odometry to loop closure and SLAM. A geometric representation based on planar patches is proposed for scene registration.Photometric information is combined with the geometric description for robustness.A novel formulation encoding uncertainty information is proposed to improve accuracy.An implementation of this work is freely available (MRPT and PCL). This paper proposes a pose-based algorithm to solve the full Simultaneous Localization And Mapping (SLAM) problem for an Autonomous Underwater Vehicle (AUV), navigating in an unknown and possibly unstructured environment. A probabilistic scan matching technique using range scans gathered from a Mechanical Scanning Imaging Sonar (MSIS) is used together with the robot dead-reckoning displacements. The proposed method utilizes two Extended Kalman Filters (EKFs). The first, estimates the local path traveled by the robot while forming the scan as well as its uncertainty, providing position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augmented state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. Also, a method of estimating the uncertainty of the scan matching estimation is provided. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach. We present a real-time SLAM system that combines an improved version of the Iterative Closest Point (ICP) and inertial dead reckoning to localize our dynamic quadrupedal machine in a local map. Despite the strong and fast motions induced by our 80 kg hydraulic legged robot, the SLAM system is robust enough to keep the position error below 5 within the local map that surrounds the robot. The 3D map of the terrain, computed at the camera frame rate is suitable for vision based planned locomotion. The inertial measurements are used before and after the ICP registration, to provide a good initial guess, to correct the output and to detect registration failures which can potentially corrupt the map. The performance in terms of time and accuracy are also doubled by preprocessing the point clouds with a background subtraction prior to performing the ICP alignment. Our local mapping approach, in spite of having a global frame of reference fixed onto the ground, aligns the current map to the body frame, and allows us to push the drift away from the most recent camera scan. The system has been tested on our robot by performing a trot around obstacles and validated against a motion capture system.