OV2SLAM on EuRoC MAV Datasets: a Study of Corner Detector Performance
2023 IEEE International Conference on Imaging Systems and Techniques (IST)(2023)
摘要
Theoretically, SLAM (Simultaneous Localization and Mapping) systems acquire information from its environment with sensors, extract landmarks from the received data and estimate its location on a map based on the sensor measurements. EuRoC datasets is a batch of visual-inertial measurements from embedded stereo camera and inertia measurement unit in a Micro Aerial Vehicle (MAV). The MAV flights include eleven itineraries and took place in indoor environments: an industrial environment and two motion capture rooms. OV
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SLAM (Online and Versatile Visual SLAM) is an open-source visual feature points-based SLAM methods that is remarkably efficient. A feature points-based method extracts and tracks keypoints because they represent stable features. The native keypoint detection method in OV
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SLAM tested in the EuRoC MAV datasets is a well-known KLT (Kanade-Lucas-Tomasi) corner detector. Nevertheless, no other detector was experimented on this SLAM method. This paper enables the investigation of which corner detector is optimum for OV
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SLAM method on the EuRoC MAV datasets. Overall, the experiments are led on 10 itineraries containing 28 058 stereo-pair images in all. Thus, by varying the parameter of the Gaussian influencing the detection of the keypoints, a global score based on different statistics is calculated in relation to the ground truth to classify which pair detector/parameter is optimal on these datasets.
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关键词
euroc mav datasets,corner
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