Results 61 to 70 of about 23,972 (233)
Stereo Event-based Visual-Inertial Odometry
, 2023 Event-based cameras are new type vision sensors whose pixels work independently and respond asynchronously to brightness change with microsecond resolution, instead of providing standard intensity frames. Compared with traditional cameras, event-based cameras have low latency, no motion blur, and high dynamic range (HDR), which provide possibilities ...
Wang, Kunfeng, Zhao, Kaichun, You, Zheng
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Direct Sparse Visual-Inertial Odometry Using Dynamic Marginalization [PDF]
2018 IEEE International Conference on Robotics and Automation (ICRA), 2018 We present VI-DSO, a novel approach for visual-inertial odometry, which jointly estimates camera poses and sparse scene geometry by minimizing photometric and IMU measurement errors in a combined energy functional. The visual part of the system performs a bundle-adjustment like optimization on a sparse set of points, but unlike key-point based systems ...
von Stumberg, Lukas +2 more
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EqVIO: An Equivariant Filter for Visual-Inertial Odometry
IEEE Transactions on Robotics, 2023 Visual Inertial Odometry (VIO) is the problem of estimating a robot's trajectory by combining information from an inertial measurement unit (IMU) and a camera, and is of great interest to the robotics community. This paper develops a novel Lie group symmetry for the VIO problem and applies the recently proposed equivariant filter. The symmetry is shown
Pieter van Goor, Robert Mahony
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High‐Speed Altitude Regulation With Neuromorphic Camera and Lightweight Embedded Computation
Advanced Intelligent Systems, EarlyView.Neuromorphic cameras deliver rapid, high‐dynamic‐range sensing but overwhelm embedded processors at high speeds. This work presents a lightweight, optimized Lucas–Kanade optical flow method with parallelization, gyroscopic derotation, and adaptive event slicing.
Simon L. Jeger +3 more
wiley +1 more source
Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning [PDF]
Computer Vision and Pattern RecognitionVisual-inertial odometry (VIO) has demonstrated re-markable success due to its low-cost and complementary sensors. However, existing VIO methods lack the general-ization ability to adjust to different environments and sen-sor attributes.
Youqi Pan +3 more
semanticscholar +1 more source
Advanced Intelligent Systems, EarlyView.Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella +5 more
wiley +1 more source
Tightly Coupled 3D Lidar Inertial Odometry and Mapping
, 2019 Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations.
Chen, Yuying, Liu, Ming, Ye, Haoyang
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A Robust Localization System for Inspection Robots in Sewer Networks † [PDF]
, 2019 Sewers represent a very important infrastructure of cities whose state should be monitored periodically. However, the length of such infrastructure prevents sensor networks from being applicable.
Alejo, David +2 more
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Advanced Intelligent Systems, EarlyView.A visual and visual‐inertial simultaneous localization and mapping (SLAM) algorithm, leveraging enhanced deep learning features and motion smoothness constraints, is proposed in this research work. This method retains the advantages of geometry‐based SLAM methods while effectively utilizing the powerful representational capabilities of data‐driven ...
Maosheng Jiang +3 more
wiley +1 more source
An Underwater SLAM System using Sonar, Visual, Inertial, and Depth Sensor
, 2019 This paper presents a novel tightly-coupled keyframe-based Simultaneous Localization and Mapping (SLAM) system with loop-closing and relocalization capabilities targeted for the underwater domain.
Li, Alberto Quattrini +2 more
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