1,720,966 research outputs found
Efficient loop closure based on FALKO lidar features for online robot localization and mapping
No full textKeypoint features detection from measurements enables efficient localization and map estimation through the compact representation and recognition of locations. The keypoint detector FALKO has been proposed to detect stable points in laser scans for localization and mapping tasks. In this paper, we present novel loop closure methods based on FALKO keypoints and compare their performance in online localization and mapping problems. The pose graph formulation is adopted, where each pose is associated to a local map of keypoints extracted from the corresponding laser scan. Loops in the graph are detected by matching local maps in two steps. First, the candidate matching scans are selected by comparing the scan signatures obtained from the keypoints of each scan. Second, the transformation between two scans is obtained by pairing and aligning the respective keypoint sets. Experiments with standard benchmark datasets assess the performance of FALKO and of the proposed loop closure algorithms in both offline and online localization and map estimation
Bio-Inspired Object Detection and Pose Estimation Algorithms for Underwater Environments
Full text linkInvestigation of Vision-Based Underwater Object Detection with Multiple Datasets
Full text linkIn this paper, we investigate the potential of vision-based object detection algorithms in underwater environments using several datasets to highlight the issues arising in different scenarios. Underwater computer vision has to cope with distortion and attenuation due to light propagation in water, and with challenging operating conditions. Scene segmentation and shape recognition in a single image must be carefully designed to achieve robust object detection and to facilitate object pose estimation. We describe a novel multi-feature object detection algorithm conceived to find human-made artefacts lying on the seabed. The proposed method searches for a target object according to a few general criteria that are robust to the underwater context, such as salient colour uniformity and sharp contours. We assess the performance of the proposed algorithm across different underwater datasets. The datasets have been obtained using stereo cameras of different quality, and diverge for the target object type and colour, acquisition depth and conditions. The effectiveness of the proposed approach has been experimentally demonstrated. Finally, object detection is discussed in connection with the simple colour-based segmentation and with the difficulty of tri-dimensional processing on noisy data
Integration of a stereo vision system into an autonomous underwater vehicle for pipe manipulation tasks
Full text linkUnderwater object detection and recognition using computer vision are challenging tasks due to the poor light condition of submerged environments. For intervention missions requiring grasping and manipulation of submerged objects, a vision system must provide an Autonomous Underwater Vehicles (AUV) with object detection, localization and tracking capabilities. In this paper, we describe the integration of a vision system in the MARIS intervention AUV and its configuration for detecting cylindrical pipes, a typical artifact of interest in underwater operations. Pipe edges are tracked using an alpha-beta filter to achieve robustness and return a reliable pose estimation even in case of partial pipe visibility. Experiments in an outdoor water pool in different light conditions show that the adopted algorithmic approach allows detection of target pipes and provides a sufficiently accurate estimation of their pose even when they become partially visible, thereby supporting the AUV in several successful pipe grasping operations
Computer vision in underwater environments: A multiscale graph segmentation approach
No full textIn this paper, we propose a novel object detection algorithm for underwater environments exploiting multiscale graph-based segmentation. The graph-based approach to image segmentation is fairly independent from distortion, color alteration and other peculiar effects arising with light propagation in water medium. The algorithm is executed at different scales in order to capture both the contour and the general shape of the target cylindrical object. Next, the candidate regions extracted at different scales are merged together. Finally, the candidate region is validated by a shape regularity test. The proposed algorithm has been compared with a color clustering method on an underwater dataset and has achieved precise and accurate detection
Performance Evaluation of a Low-Cost Stereo Vision System for Underwater Object Detection
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Issues in high performance vision systems design for underwater interventions
No full textThis paper describes the design and evaluation of a vision system conceived to provide perception in Autonomous Underwater Vehicle (AUV) intervention tasks. Due to the accuracy requirements inherent in manipulation tasks, high performance vision systems, enabling adequate perception capabilities, are needed to cope with underwater interventions. However, vision systems are challenged by the difficulties and variability of underwater environments as well as by the need to operate in a sealed canister. The vision system described in this paper addresses design issues like computational performance, energy power consumption, heat dissipation, and network capabilities. Even though the system has been designed to support stereovision, experiments in several underwater contexts have shown that stereovision is seldom applicable, due to the many problems faced by light propagation in water. Developing a system for underwater operation emphasizes the need for tradeoffs between computational performance and power consumption and dissipation, as well as the need for flexibility to support multiple vision processing pipelines and adapt to the specific underwater context
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