Results 21 to 30 of about 6,675 (223)
Multimotion Visual Odometry (MVO)
CoRR, 2021 Visual motion estimation is a well-studied challenge in autonomous navigation. Recent work has focused on addressing multimotion estimation in highly dynamic environments. These environments not only comprise multiple, complex motions but also tend to exhibit significant occlusion.
Kevin M. Judd, Jonathan D. Gammell
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Real-Time Monocular Visual Odometry for Turbid and Dynamic Underwater Environments [PDF]
Sensors, 2019 Maxime Ferrera +2 more
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Deep Direct Visual Odometry [PDF]
IEEE Transactions on Intelligent Transportation Systems, 2022 Traditional monocular direct visual odometry (DVO) is one of the most famous methods to estimate the ego-motion of robots and map environments from images simultaneously. However, DVO heavily relies on high-quality images and accurate initial pose estimation during tracking.
Chaoqiang Zhao +3 more
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Road-Network-Map-Assisted Vehicle Positioning Based on Pose Graph Optimization
Sensors, 2023 Satellite signals are easily lost in urban areas, which causes difficulty in vehicles being located with high precision. Visual odometry has been increasingly applied in navigation systems to solve this problem.
Shuchen Xu +4 more
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Robocentric Visual-Inertial Odometry [PDF]
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018 In this paper, we propose a novel robocentric formulation of the visual–inertial navigation system (VINS) within a sliding-window filtering framework and design an efficient, lightweight, robocentric visual–inertial odometry (R-VIO) algorithm for consistent motion tracking even in challenging environments using only a monocular camera and a six-axis ...
Zheng Huai, Guoquan Huang 0001
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Chinese Journal of Mechanical Engineering, 2023 Visual odometry is critical in visual simultaneous localization and mapping for robot navigation. However, the pose estimation performance of most current visual odometry algorithms degrades in scenes with unevenly distributed features because dense ...
Chang Wang +4 more
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Multi-Sensor Fusion Self-Supervised Deep Odometry and Depth Estimation
Remote Sensing, 2022 This paper presents a new deep visual-inertial odometry and depth estimation framework for improving the accuracy of depth estimation and ego-motion from image sequences and inertial measurement unit (IMU) raw data.
Yingcai Wan +4 more
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A Unified Formulation for Visual Odometry [PDF]
2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019 Monocular Odometry systems can be broadly categorized as being either Direct, Indirect, or a hybrid of both. While Indirect systems process an alternative image representation to compute geometric residuals, Direct methods process the image pixels directly to generate photometric residuals.
Georges Younes 0001 +2 more
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Bias compensation in visual odometry [PDF]
2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2012 Empirical evidence shows that error growth in visual odometry is biased. A projective bias model is developed and its parameters are estimated offline from trajectories encompassing loops. The model is used online to compensate for bias and thereby significantly reduces error growth.
Gijs Dubbelman +2 more
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Marked-LIEO: Visual Marker-Aided LiDAR/IMU/Encoder Integrated Odometry
Sensors, 2022 In this paper, we propose a visual marker-aided LiDAR/IMU/encoder integrated odometry, Marked-LIEO, to achieve pose estimation of mobile robots in an indoor long corridor environment. In the first stage, we design the pre-integration model of encoder and
Baifan Chen +3 more
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