Results 201 to 210 of about 23,972 (233)

Visual inertial odometry using coupled nonlinear optimization

2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017
Visual inertial odometry (VIO) gained lots of interest recently for efficient and accurate ego-motion estimation of robots and automobiles. With a monocular camera and an inertial measurement unit (IMU) rigidly attached, VIO aims to estimate the 3D pose trajectory of the device in a global metric space.
Euntae Hong, Jongwoo Lim
openaire   +1 more source

DDIO-Mapping: A Fast and Robust Visual-Inertial Odometry for Low-Texture Environment Challenge

IEEE Transactions on Industrial Informatics
Accurate localization and pose estimation remain challenging for autonomous robots in low-texture environment. This article proposes a tightly coupled direct depth-inertial odometry and mapping (DDIO-Mapping) framework to simultaneously tackle three ...
Xinyu Jiang, Heng Li, Chuangquan Chen, Yongquan Chen, Junlang Huang, Zuguang Zhou, Yimin Zhou, C. Vong   +7 more
semanticscholar   +1 more source

Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry

arXiv.org
In recent years, transformer-based architectures become the de facto standard for sequence modeling in deep learning frameworks. Inspired by the successful examples, we propose a causal visual-inertial fusion transformer (VIFT) for pose estimation in ...
Yunus Bilge Kurt, A. Akman, A. Alatan
semanticscholar   +1 more source

A Spatiotemporal Hand-Eye Calibration for Trajectory Alignment in Visual(-Inertial) Odometry Evaluation

IEEE Robotics and Automation Letters
A common prerequisite for evaluating a visual (-inertial) odometry (VO/VIO) algorithm is to align the timestamps and the reference frame of its estimated trajectory with a reference ground-truth derived from a system of superior precision, such as a ...
Zichao Shu, Lijun Li, Rui Wang, Zetao Chen   +3 more
semanticscholar   +1 more source

IMU Augment Tightly Coupled Lidar-Visual-Inertial Odometry for Agricultural Environments

IEEE Robotics and Automation Letters
This letter presents a new tightly coupled LiDAR-visual-inertial odometry scheme for agricultural autonomous machinery under a structureless environment and the presence of fluctuation uncertainties.
Quoc Hung Hoang, Gon-Woo Kim
semanticscholar   +1 more source

A Fast and Accurate Visual Inertial Odometry Using Hybrid Point-Line Features

IEEE Robotics and Automation Letters
Mainstream visual-inertial SLAM systems use point features for motion estimation and localization. However, point features do not perform well in scenes such as weak texture and motion blur. Therefore, the introduction of line features has received a lot
Zhenhang Chen, Zhiqiang Miao, Min Liu, Chengzhong Wu, Yaonan Wang   +4 more
semanticscholar   +1 more source

RMSC-VIO: Robust Multi-Stereoscopic Visual-Inertial Odometry for Local Visually Challenging Scenarios

IEEE Robotics and Automation Letters
We present a Multi-Stereoscopic Visual-Inertial Odometry (VIO) system capable of integrating an arbitrary number of stereo cameras, exhibiting excellent robustness in the face of visually challenging scenarios.
Tong Zhang, Jianyu Xu, Hao Shen, Rui Yang, Tao Yang   +4 more
semanticscholar   +1 more source

Robust Neural Visual Inertial Odometry With Deep Velocity Constraint

IEEE Robotics and Automation Letters
Visual inertial odometry (VIO) is a popular solution for localization in the GPS-denied environment. However, current VIO algorithms' performance degrades in the visual degraded environment due to limited visual features. This paper presents a robust VIO
Pengfei Gu, Pengkun Zhou, Ziyang Meng
semanticscholar   +1 more source

Visual-Inertial Odometry for Metric-Scale Mapping of Underwater Caves

2024 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Mapping underwater caves is a challenging endeavor. Complex morphology and fragile geological formations limit the use of tethered ROV or AUV platforms for data collection. Trained cave divers remain the most effective means to achieve this task.
Arthur Lago, Nuno Neves, Rodrigo M. M. Ventura, Jośe Gaspar   +3 more
semanticscholar   +1 more source

DOGE: An Extrinsic Orientation and Gyroscope Bias Estimation for Visual-Inertial Odometry Initialization

IEEE International Conference on Robotics and Automation
Most existing visual-inertial odometry (VIO) initialization methods rely on accurate pre-calibrated extrinsic parameters. However, during long-term use, irreversible structural deformation caused by temperature changes, mechanical squeezing, etc.
Zewen Xu, Yijia He, Hao Wei, Yihong Wu
semanticscholar   +1 more source