1,721,063 research outputs found
A robust structure and motion replacement for bundle adjustment
No full textThis paper demonstrates the application of a robust form of pose estimation and scene reconstruction using data from camera images. We demonstrate results that suggest the ability of the algorithm to rival methods of RANSAC-based pose estimation polished by bundle adjustment in terms of solution robustness, speed and accuracy, even when given poor initialisations. Our simulated results show the behaviour of the algorithm in a number of novel simulated scenarios reflective of real world cases that show the ability of the algorithm to handle large observation noise and difficult reconstruction scenes. These results have a number of implications for the vision and robotics community, and show that the application of visual motion estimation on robotic platforms in an online fashion is approaching real-world feasibility
Improving recall in appearance-based visual SLAM using visual expectation
No full textIn this paper, we present a new algorithm for boosting visual template recall performance through a process of visual expectation. Visual expectation dynamically modifies the recognition thresholds of learnt visual templates based on recently matched templates, improving the recall of sequences of familiar places while keeping precision high, without any feedback from a mapping backend. We demonstrate the performance benefits of visual expectation using two 17 kilometer datasets gathered in an outdoor environment at two times separated by three weeks. The visual expectation algorithm provides up to a 100% improvement in recall. We also combine the visual expectation algorithm with the RatSLAM SLAM system and show how the algorithm enables successful mappin
Kelp detection in highly dynamic environments using texture recognition
No full textThis paper describes a texture recognition based method for segmenting kelp from images collected in highly dynamic shallow water environments by an Autonomous Underwater Vehicle (AUV). A particular challenge is image quality that is affected by uncontrolled lighting, reduced visibility, significantly varying perspective due to platform egomotion, and kelp sway from wave action. The kelp segmentation approach uses the Mahalanobis distance as a way to classify Haralick texture features from sub-regions within an image. The results illustrate the applicability of the method to classify kelp allowing construction of probability maps of kelp masses across a sequence of images
Loop closure detection on a suburban road network using a continuous appearance-based trajectory
No full textThis paper presents a novel technique for performing SLAM along a continuous trajectory of appearance. Derived from components of FastSLAM and FAB-MAP, the new system dubbed Continuous Appearance-based Trajectory SLAM (CAT-SLAM) augments appearancebased place recognition with particle-filter based ‘pose filtering’ within a probabilistic framework, without calculating global feature geometry or\ud
performing 3D map construction. For loop closure detection CAT-SLAM updates in constant time regardless of map size. We evaluate the effectiveness of CAT-SLAM on a 16km outdoor road network and determine its loop closure performance relative to FAB-MAP. CAT-SLAM recognizes 3 times the number of loop closures for the case where no false positives\ud
occur, demonstrating its potential use for robust loop closure detection in large environments
Reliable automatic camera-laser calibration
No full textCamera-laser calibration is necessary for many robotics and computer vision applications. However, existing calibration toolboxes still require laborious effort from the operator in order to achieve reliable and accurate results. This paper proposes algorithms that augment two existing trustful calibration methods with an automatic extraction of the calibration object from the sensor data. The result is a complete procedure that allows for automatic camera-laser calibration. The first stage of the procedure is automatic camera calibration which is useful in its own right for many applications. The chessboard extraction algorithm it provides is shown to outperform openly available techniques. The second stage completes the procedure by providing automatic camera-laser calibration. The procedure has been verified by extensive experimental tests with the proposed algorithms providing a major reduction in time required from an operator in comparison to manual methods
Practical application of pseudospectral optimization to robot path planning
No full textTo obtain minimum time or minimum energy trajectories for robots it is necessary to employ planning methods which adequately consider the platform’s dynamic properties. A variety of sampling, graph-based or local receding-horizon optimisation methods have previously been proposed. These typically use simplified kino-dynamic models to avoid the significant computational burden of solving this problem in a high dimensional state-space. In this paper we investigate solutions from the class of pseudospectral optimisation methods which have grown in favour amongst the optimal control community in recent years. These methods have high computational efficiency and rapid convergence properties. We present a practical application of such an approach to the robot path planning problem to provide a trajectory considering the robot’s dynamic properties. We extend the existing literature by augmenting the path constraints with sensed obstacles rather than predefined analytical functions to enable real world application
Long-range stereo visual odometry for extended altitude flight of unmanned aerial vehicles
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Visual Odometry is a key technology for robust and accurate navigation of unmanned aerial vehicles in a number of low altitude applications (<120 m), particularly in environments where access to a global positioning system is possible, but not guaranteed. Navigating via vision alone reduces dependence on a global positioning system and other global navigation satellite systems, enhancing navigation robustness even in the presence of jamming, spoofing or long dropouts. To date, however, most demonstrations of visual odometry are in close proximity to the ground or other structures and are often implemented as a monocular camera combined with inertial sensing, rather than vision alone, to account for scale drift. Stereo visual odometry has received little attention for applications beyond 30 m altitude due to the generally poor performance of stereo rigs for these extremely small baseline-to-depth ratios, otherwise termed long-range stereo. This paper demonstrates stereo visual pose estimation at altitudes of up to 120 m above ground level on a small fixed-wing unmanned aerial vehicle by adapting the traditional stereo visual odometry paradigm to explicitly account for inaccurate triangulation and poorly observed scale from the stereo baseline. In addition, issues related to long-range stereo such as biased sensing are investigated to justify the approach, and a novel bundle adjustment algorithm is presented capable of handling vibration induced structural deformation between the cameras. This is achieved by continually optimizing the stereo transform within a set of inequality bounds. Results are presented demonstrating the algorithm on field-gathered data from a 2 m wingspan fixed-wing unmanned aerial vehicle flying at 30-120 m altitude over a 6.5 km trajectory
Grounding action in visuo-haptic space using experience networks
No full textTraditional approaches to the use of machine learning algorithms do not provide a method to learn multiple tasks in one-shot on an embodied robot. It is proposed that grounding actions within the sensory space leads to the development of action-state relationships which can be re-used despite a change in task. A novel approach called an Experience Network is developed and assessed on a real-world robot required to perform three separate tasks. After grounded representations were developed in the initial task, only minimal further learning was required to perform the second and third task
High altitude stereo visual odometry
No full textStereo visual odometry has received little investigation in high altitude applications due to the generally poor performance of rigid stereo rigs at extremely small baseline-to-depth ratios. Without additional sensing, metric scale is considered lost and odometry is seen as effective only for monocular perspectives. This paper presents a novel modification to stereo based visual odometry that allows accurate, metric pose estimation from high altitudes, even in the presence of poor calibration and without additional sensor inputs. By relaxing the (typically fixed) stereo transform during bundle adjustment and reducing the dependence on the fixed geometry for triangulation, metrically scaled visual odometry can be obtained in situations where high altitude and structural deformation from vibration would cause traditional algorithms to fail. This is achieved through the use of a novel constrained bundle adjustment routine and accurately scaled pose initializer. We present visual odometry results demonstrating the technique on a short-baseline stereo pair inside a fixed-wing UAV flying at significant height (~30-100m)
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