waashk/dblp huggingface dataset:
generation and visualization of large-scale three-dimensional reconstructions from underwater robotic surveys. robust, scalable simultaneous localization and mapping (slam) algorithms support the successful deployment of robots in real-world applications. in many cases these platforms deliver vast amounts of sensor data from large-scale, unstructured environments. these data may be difficult to interpret by end users without further processing and suitable visualization tools. we present a robust, automated system for large-scale three-dimensional (3d) reconstruction and visualization that takes stereo imagery from an autonomous underwater vehicle (auv) and slam-based vehicle poses to deliver detailed 3d models of the seafloor in the form of textured polygonal meshes. our system must cope with thousands of images, lighting conditions that create visual seams when texturing, and possible inconsistencies between stereo meshes arising from errors in calibration, triangulation, and navigation. our approach breaks down the problem into manageable stages by first estimating local structure and then combining these estimates to recover a composite georeferenced structure using slam-based vehicle pose estimates. a texture-mapped surface at multiple scales is then generated that is interactively presented to the user through a visualization engine. we adapt established solutions when possible, with an emphasis on quickly delivering approximate yet visually consistent reconstructions on standard computing hardware. this allows scientists on a research cruise to use our system to design follow-up deployments of the auv and complementary instruments. to date, this system has been tested on several research cruises in australian waters and has been used to reliably generate and visualize reconstructions for more than 60 dives covering diverse habitats and representing hundreds of linear kilometers of survey. &copy; 2009 wiley periodicals, inc.
kinematic reconfigurability control for an environmental mobile robot operating in the amazon rain forest. this paper addresses the active control problem of reconfigurable mobile robots on irregular terrain. different kinematic control strategies to improve robot mobility are proposed based on (a) ground clearance and orientation, (b) ground clearance and stability margin, and (c) wheel traction efficiency. owing to conflicting objectives associated with these strategies, a multi-objective optimization approach is formulated to determine a set of optimal solutions and establish a trade-off optimal solution. the proposed control is validated through numerical simulations and experimental tests using an amphibious wheel-legged robot, named environmental hybrid robot, designed for environmental monitoring in the amazon rain forest. &copy; 2010 wiley periodicals, inc. the acm portal is published by the association for computing machinery. copyright © 2010 acm, inc. terms of usage privacy policy code of ethics contact us useful downloads: adobe acrobat quicktime windows media player real player
error modeling and calibration of exteroceptive sensors for accurate mapping applications. reliable robotic perception and planning are critical to performing autonomous actions in uncertain, unstructured environments. in field robotic systems, automation is achieved by interpreting exteroceptive sensor information to infer something about the world. this is then mapped to provide a consistent spatial context, so that actions can be planned around the predicted future interaction of the robot and the world. the whole system is as reliable as the weakest link in this chain. in this paper, the term mapping is used broadly to describe the transformation of range-based exteroceptive sensor data (such as lidar or stereo vision) to a fixed navigation frame, so that it can be used to form an internal representation of the environment. the coordinate transformation from the sensor frame to the navigation frame is analyzed to produce a spatial error model that captures the dominant geometric and temporal sources of mapping error. this allows the mapping accuracy to be calculated at run time. a generic extrinsic calibration method for exteroceptive range-based sensors is then presented to determine the sensor location and orientation. this allows systematic errors in individual sensors to be minimized, and when multiple sensors are used, it minimizes the systematic contradiction between them to enable reliable multisensor data fusion. the mathematical derivations at the core of this model are not particularly novel or complicated, but the rigorous analysis and application to field robotics seems to be largely absent from the literature to date. the techniques in this paper are simple to implement, and they offer a significant improvement to the accuracy, precision, and integrity of mapped information. consequently, they should be employed whenever maps are formed from range-based exteroceptive sensor data. &copy; 2009 wiley periodicals, inc.
efficient three-dimensional scene modeling and mosaicing. scene modeling has a key role in applications ranging from visual mapping to augmented reality. this paper presents an end-to-end solution for creating accurate three-dimensional (3d) textured models using monocular video sequences. the methods are developed within the framework of sequential structure from motion, in which a 3d model of the environment is maintained and updated as new visual information becomes available. the proposed approach contains contributions at different levels. the camera pose is recovered by directly associating the 3d scene model with local image observations, using a dual-registration approach. compared to the standard structure from motion techniques, this approach decreases the error accumulation while increasing the robustness to scene occlusions and feature association failures, while allowing 3d reconstructions for any type of scene. motivated by the need to map large areas, a novel 3d vertex selection mechanism is proposed, which takes into account the geometry of the scene. vertices are selected not only to have high reconstruction accuracy but also to be representative of the local shape of the scene. this results in a reduction in the complexity of the final 3d model, with minimal loss of precision. as a final step, a composite visual map of the scene (mosaic) is generated. we present a method for blending image textures using 3d geometric information and photometric differences between registered textures. the method allows high-quality mosaicing over 3d surfaces by reducing the effects of the distortions induced by camera viewpoint and illumination changes. the results are presented for four scene modeling scenarios, including a comparison with ground truth under a realistic scenario and a challenging underwater data set. although developed primarily for underwater mapping applications, the methods are general and applicable to other domains, such as aerial and land-based mapping. &copy; 2009 wiley periodicals, inc.
online three-dimensional slam by registration of large planar surface segments and closed-form pose-graph relaxation. a fast pose-graph relaxation technique is presented for enhancing the consistency of three-dimensional (3d) maps created by registering large planar surface patches. the surface patches are extracted from point clouds sampled from a 3d range sensor. the plane-based registration method offers an alternative to the state-of-the-art algorithms and provides advantages in terms of robustness, speed, and storage. one of its features is that it results in an accurate determination of rotation, although a lack of predominant surfaces in certain directions may result in translational uncertainty in those directions. hence, a loop-closing and relaxation problem is formulated that gains significant speed by relaxing only the translational errors and utilizes the full-translation covariance determined during pairwise registration. this leads to a fast 3d simultaneous localization and mapping suited for online operations. the approach is tested in two disaster scenarios that were mapped at the nist 2008 response robot evaluation exercise in disaster city, texas. the two data sets from a collapsed car park and a flooding disaster consist of 26 and 70 3d scans, respectively. the results of these experiments show that our approach can generate 3d maps without motion estimates by odometry and that it outperforms iterative closest point&ndash;based mapping with respect to speed and robustness. &copy; 2009 wiley periodicals, inc.
automatic appearance-based loop detection from three-dimensional laser data using the normal distributions transform. we propose a new approach to appearance-based loop detection for mobile robots, using three-dimensional (3d) laser scans. loop detection is an important problem in the simultaneous localization and mapping (slam) domain, and, because it can be seen as the problem of recognizing previously visited places, it is an example of the data association problem. without a flat-floor assumption, two-dimensional laser-based approaches are bound to fail in many cases. two of the problems with 3d approaches that we address in this paper are how to handle the greatly increased amount of data and how to efficiently obtain invariance to 3d rotations. we present a compact representation of 3d point clouds that is still discriminative enough to detect loop closures without false positives (i.e., detecting loop closure where there is none). a low false-positive rate is very important because wrong data association could have disastrous consequences in a slam algorithm. our approach uses only the appearance of 3d point clouds to detect loops and requires no pose information. we exploit the normal distributions transform surface representation to create feature histograms based on surface orientation and smoothness. the surface shape histograms compress the input data by two to three orders of magnitude. because of the high compression rate, the histograms can be matched efficiently to compare the appearance of two scans. rotation invariance is achieved by aligning scans with respect to dominant surface orientations. we also propose to use expectation maximization to fit a gamma mixture model to the output similarity measures in order to automatically determine the threshold that separates scans at loop closures from nonoverlapping ones. we discuss the problem of determining ground truth in the context of loop detection and the difficulties in comparing the results of the few available methods based on range information. furthermore, we present quantitative performance evaluations using three real-world data sets, one of which is highly self-similar, showing that the proposed method achieves high recall rates (percentage of correctly identified loop closures) at low false-positive rates in environments with different characteristics. &copy; 2009 wiley periodicals, inc.