Applied registration for robotics: Methodology and tools for ICP-like algorithms
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Cited by 2 publications
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The paper addresses the problem of camera tracking, which denotes the continuous image-based computation of a camera's position and orientation with respect to a reference frame. The method aims at regular cameras, which means that 3D-3D registration methods applicable to RGB-D cameras are not an option. The tracked frame contains only 2D information, thus requiring a solution to the absolute pose or 2D-3D registration problem.While traditional solutions to camera tracking [3] rely on sparse feature correspondences, the community has recently seen a number of direct photometric registration methods such as Newcombe et al. [8] and Engel et al. [1]. [1] is conceptually similar to [8], however gains computational efficiency by reducing the computation from dense to semi-dense regions that correspond to a thresholded edge-map of the image.Photometric methods have the more general advantage of compensating for appearance variations caused by perspective view-point changes, whereas classical sparse methods often rely on static feature descriptors only (providing at most rotation and scale invariant properties [5,6]). However, photometric registration techniques inherently suffer from the disability to overcome large disparities, where large sometimes means even just a couple of pixels [7]. Many photometric registration techniques therefore depend on pyramidal subsampling schemes in order to alleviate this problem.The goal of the present paper is a novel 2D-3D registration paradigm for semi-dense depth maps that relies on the Iterative Closest Point (ICP) technique, and thus a reintroduction of geometric error minimization as a valid alternative for real-time monocular camera tracking in the case of semi-dense features. An example semi-dense depth map is indicated in Figure 1. In comparison to photometric registration techniques, our ICP technique has the conceptual advantage of requiring neither isotropic enlargement of the employed semi-dense regions, nor pyramidal subsampling. The work is in line with Feldmar et al. [2], Tomono [9], and Klein and Murray [4], which already attempt curve or edge registration in 2D using ICP.Based on a hypothesized relative pose, the basic idea consists of warping a reference curve into the tracked image based on a prior 3D model or depth (in our case semi-dense) inside a reference frame. From a mathematical point of view, our idea may be formulated as follows. Let)} denote the semi-dense depth map, where P = {p i } is the set of pixel locations that defines the semi-dense region in the reference frame F k , d i the inverse depth of a pixel, and π(p i ) = f i is a known function that transforms points in the image plane into unit direction vectors located on the unit sphere around the camera center. The warped semi-dense region is easily obtained byi − t }, where t and R denote the seeked position and orientation of the current frame. The final objective results in) is a function that returns the pixel from P F k+1 that is closest to ounder the Euclidean distance metric. We propose ite...
The paper addresses the problem of camera tracking, which denotes the continuous image-based computation of a camera's position and orientation with respect to a reference frame. The method aims at regular cameras, which means that 3D-3D registration methods applicable to RGB-D cameras are not an option. The tracked frame contains only 2D information, thus requiring a solution to the absolute pose or 2D-3D registration problem.While traditional solutions to camera tracking [3] rely on sparse feature correspondences, the community has recently seen a number of direct photometric registration methods such as Newcombe et al. [8] and Engel et al. [1]. [1] is conceptually similar to [8], however gains computational efficiency by reducing the computation from dense to semi-dense regions that correspond to a thresholded edge-map of the image.Photometric methods have the more general advantage of compensating for appearance variations caused by perspective view-point changes, whereas classical sparse methods often rely on static feature descriptors only (providing at most rotation and scale invariant properties [5,6]). However, photometric registration techniques inherently suffer from the disability to overcome large disparities, where large sometimes means even just a couple of pixels [7]. Many photometric registration techniques therefore depend on pyramidal subsampling schemes in order to alleviate this problem.The goal of the present paper is a novel 2D-3D registration paradigm for semi-dense depth maps that relies on the Iterative Closest Point (ICP) technique, and thus a reintroduction of geometric error minimization as a valid alternative for real-time monocular camera tracking in the case of semi-dense features. An example semi-dense depth map is indicated in Figure 1. In comparison to photometric registration techniques, our ICP technique has the conceptual advantage of requiring neither isotropic enlargement of the employed semi-dense regions, nor pyramidal subsampling. The work is in line with Feldmar et al. [2], Tomono [9], and Klein and Murray [4], which already attempt curve or edge registration in 2D using ICP.Based on a hypothesized relative pose, the basic idea consists of warping a reference curve into the tracked image based on a prior 3D model or depth (in our case semi-dense) inside a reference frame. From a mathematical point of view, our idea may be formulated as follows. Let)} denote the semi-dense depth map, where P = {p i } is the set of pixel locations that defines the semi-dense region in the reference frame F k , d i the inverse depth of a pixel, and π(p i ) = f i is a known function that transforms points in the image plane into unit direction vectors located on the unit sphere around the camera center. The warped semi-dense region is easily obtained byi − t }, where t and R denote the seeked position and orientation of the current frame. The final objective results in) is a function that returns the pixel from P F k+1 that is closest to ounder the Euclidean distance metric. We propose ite...
3 DoF/6 DoF Localization System for Low Computing Power Mobile Robot Platforms
Mobile robot platforms have a wide range of hardware configurations in order to accomplish challenging tasks and require an efficient and accurate localization system to navigate in the environment. The objective of this work is the evaluation of the developed Dynamic Robot Localization (DRL) system in three computing platforms, with CPUs ranging from low to high end (Intel Atom, Core i5, and i7), in order to analyze the configurations that can be used to adjust the trade-offs between pose estimation accuracy and the associated computing resources required. The DRL is capable of performing pose tracking and global pose estimation in both 3 and 6 Degrees of Freedom (DoF) using point cloud data retrieved from LIDARs and RGB-D cameras and achieved translation errors of less than 30 mm and rotation errors of less than 5° when evaluated in three environments. The sensor data retrieved from three testing platforms was processed and the detailed profiling results were analyzed. Besides pose estimation, the self-localization system is also able to perform mapping of the environment with probabilistic integration or removal of geometry and can use surface reconstruction to minimize the impact of sensor noise. These abilities will allow the fast deployment of mobile robots in dynamic environments.
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