8,838 research outputs found
Efficient Continuous-Time SLAM for 3D Lidar-Based Online Mapping
Modern 3D laser-range scanners have a high data rate, making online
simultaneous localization and mapping (SLAM) computationally challenging.
Recursive state estimation techniques are efficient but commit to a state
estimate immediately after a new scan is made, which may lead to misalignments
of measurements. We present a 3D SLAM approach that allows for refining
alignments during online mapping. Our method is based on efficient local
mapping and a hierarchical optimization back-end. Measurements of a 3D laser
scanner are aggregated in local multiresolution maps by means of surfel-based
registration. The local maps are used in a multi-level graph for allocentric
mapping and localization. In order to incorporate corrections when refining the
alignment, the individual 3D scans in the local map are modeled as a sub-graph
and graph optimization is performed to account for drift and misalignments in
the local maps. Furthermore, in each sub-graph, a continuous-time
representation of the sensor trajectory allows to correct measurements between
scan poses. We evaluate our approach in multiple experiments by showing
qualitative results. Furthermore, we quantify the map quality by an
entropy-based measure.Comment: In: Proceedings of the International Conference on Robotics and
Automation (ICRA) 201
Benchmarking and Comparing Popular Visual SLAM Algorithms
This paper contains the performance analysis and benchmarking of two popular
visual SLAM Algorithms: RGBD-SLAM and RTABMap. The dataset used for the
analysis is the TUM RGBD Dataset from the Computer Vision Group at TUM. The
dataset selected has a large set of image sequences from a Microsoft Kinect
RGB-D sensor with highly accurate and time-synchronized ground truth poses from
a motion capture system. The test sequences selected depict a variety of
problems and camera motions faced by Simultaneous Localization and Mapping
(SLAM) algorithms for the purpose of testing the robustness of the algorithms
in different situations. The evaluation metrics used for the comparison are
Absolute Trajectory Error (ATE) and Relative Pose Error (RPE). The analysis
involves comparing the Root Mean Square Error (RMSE) of the two metrics and the
processing time for each algorithm. This paper serves as an important aid in
the selection of SLAM algorithm for different scenes and camera motions. The
analysis helps to realize the limitations of both SLAM methods. This paper also
points out some underlying flaws in the used evaluation metrics.Comment: 7 pages, 4 figure
Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age
Simultaneous Localization and Mapping (SLAM)consists in the concurrent
construction of a model of the environment (the map), and the estimation of the
state of the robot moving within it. The SLAM community has made astonishing
progress over the last 30 years, enabling large-scale real-world applications,
and witnessing a steady transition of this technology to industry. We survey
the current state of SLAM. We start by presenting what is now the de-facto
standard formulation for SLAM. We then review related work, covering a broad
set of topics including robustness and scalability in long-term mapping, metric
and semantic representations for mapping, theoretical performance guarantees,
active SLAM and exploration, and other new frontiers. This paper simultaneously
serves as a position paper and tutorial to those who are users of SLAM. By
looking at the published research with a critical eye, we delineate open
challenges and new research issues, that still deserve careful scientific
investigation. The paper also contains the authors' take on two questions that
often animate discussions during robotics conferences: Do robots need SLAM? and
Is SLAM solved
Hand Keypoint Detection in Single Images using Multiview Bootstrapping
We present an approach that uses a multi-camera system to train fine-grained
detectors for keypoints that are prone to occlusion, such as the joints of a
hand. We call this procedure multiview bootstrapping: first, an initial
keypoint detector is used to produce noisy labels in multiple views of the
hand. The noisy detections are then triangulated in 3D using multiview geometry
or marked as outliers. Finally, the reprojected triangulations are used as new
labeled training data to improve the detector. We repeat this process,
generating more labeled data in each iteration. We derive a result analytically
relating the minimum number of views to achieve target true and false positive
rates for a given detector. The method is used to train a hand keypoint
detector for single images. The resulting keypoint detector runs in realtime on
RGB images and has accuracy comparable to methods that use depth sensors. The
single view detector, triangulated over multiple views, enables 3D markerless
hand motion capture with complex object interactions.Comment: CVPR 201
Viewfinder: final activity report
The VIEW-FINDER project (2006-2009) is an 'Advanced Robotics' project that seeks to apply a semi-autonomous robotic system to inspect ground safety in the event of a fire. Its primary aim is to gather data (visual and chemical) in order to assist rescue personnel. A base station combines the gathered information with information retrieved from off-site sources.
The project addresses key issues related to map building and reconstruction, interfacing local command information with external sources, human-robot interfaces and semi-autonomous robot navigation.
The VIEW-FINDER system is a semi-autonomous; the individual robot-sensors operate autonomously within the limits of the task assigned to them, that is, they will autonomously navigate through and inspect an area. Human operators monitor their operations and send high level task requests as well as low level commands through the interface to any nodes in the entire system. The human interface has to ensure the human supervisor and human interveners are provided a reduced but good and relevant overview of the ground and the robots and human rescue workers therein
Image-based 3-D reconstruction of constrained environments
Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions.Nuclear power plays a important role to the United Kingdom electricity generation infrastructure, providing a reliable baseload of low carbon electricity. The Advanced Gas-cooled Reactor (AGR) design makes up approximately 50% of the existing fleet, however, many of the operating reactors have exceeding their original design lifetimes.To ensure safe reactor operation, engineers perform periodic in-core visual inspections of reactor components to monitor the structural health of the core as it ages. However, current inspection mechanisms deployed provide limited structural information about the fuel channel or defects.;This thesis investigates the suitability of image-based 3-D reconstruction techniques to acquire 3-D structural geometry to enable improved diagnostic and prognostic abilities for inspection engineers. The application of image-based 3-D reconstruction to in-core inspection footage highlights significant challenges, most predominantly that the image saliency proves insuffcient for general reconstruction frameworks. The contribution of the thesis is threefold. Firstly, a novel semi-dense matching scheme which exploits sparse and dense image correspondence in combination with a novel intra-image region strength approach to improve the stability of the correspondence between images.;This results in a percentage increase of 138.53% of correct feature matches over similar state-of-the-art image matching paradigms. Secondly, a bespoke incremental Structure-from-Motion (SfM) framework called the Constrained Homogeneous SfM (CH-SfM) which is able to derive structure from deficient feature spaces and constrained environments. Thirdly, the application of the CH-SfM framework to remote visual inspection footage gathered within AGR fuel channels, outperforming other state-of-the-art reconstruction approaches and extracting representative 3-D structural geometry of orientational scans and fully circumferential reconstructions.;This is demonstrated on in-core and laboratory footage, achieving an approximate 3-D point density of 2.785 - 23.8025NX/cm² for real in-core inspection footage and high quality laboratory footage respectively. The demonstrated novelties have applicability to other constrained or feature-poor environments, with future work looking to producing fully dense, photo-realistic 3-D reconstructions
A hierarchical strategy for real-time tracking on-board UAVs
In this paper, we present a real-time tracking strategy based on direct methods for tracking tasks on-board UAVs, that is able to overcome problems posed by the challenging conditions of the task: e.g. constant vibrations, fast 3D changes, and limited capacity on-board. The vast majority of approaches make use of feature-based methods to track objects. Nonetheless, in this paper we show that although some of these feature-based solutions are faster, direct methods can be more robust under fast 3D motions (fast changes in position), some changes in appearance, constant vibrations (without requiring any specific hardware or software for video stabilization), and situations where part of the object to track is out the field of view of the camera. The performance of the proposed strategy is evaluated with images from real-flight tests using different evaluation mechanisms (e.g. accurate position estimation using a Vicon sytem). Results show that our tracking strategy performs better than well known feature-based algorithms and well known configurations of direct methods, and that the recovered data is robust enough for vision-in-the-loop tasks
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