Results 31 to 40 of about 2,723 (200)

Low drift visual inertial odometry with UWB aided for indoor localization

open access: yesIET Communications, 2022
Visual inertial odometry (VIO) would have an estimation drift problem in the process of long trajectory for indoor localization, especially in the absence of loop detection or in unknown complex scenes.
Bo Gao   +3 more
doaj   +1 more source

An Attention-Based Odometry Framework for Multisensory Unmanned Ground Vehicles (UGVs)

open access: yesDrones, 2023
Recently, deep learning methods and multisensory fusion have been applied to address odometry challenges in unmanned ground vehicles (UGVs). In this paper, we propose an end-to-end visual-lidar-inertial odometry framework to enhance the accuracy of pose ...
Zhiyao Xiao, Guobao Zhang
doaj   +1 more source

KEYFRAME-BASED VISUAL-INERTIAL SLAM USING NONLINEAR OPTIMIZATION [PDF]

open access: yes, 2013
The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non ...
Konolige, Kurt   +19 more
core   +1 more source

Adaptive Monocular Visual–Inertial SLAM for Real-Time Augmented Reality Applications in Mobile Devices

open access: yesSensors, 2017
Simultaneous localization and mapping (SLAM) is emerging as a prominent issue in computer vision and next-generation core technology for robots, autonomous navigation and augmented reality.
Jin-Chun Piao, Shin-Dug Kim
doaj   +1 more source

Multisensor Data Fusion for Robust Pose Estimation of a Six-Legged Walking Robot [PDF]

open access: yes, 2011
For autonomous navigation tasks it is important that the robot always has a good estimate of its current pose with respect to its starting position and - in terms of orientation - with respect to the gravity vector.
Chilian, Annett   +5 more
core   +1 more source

VILO SLAM: Tightly Coupled Binocular Vision–Inertia SLAM Combined with LiDAR

open access: yesSensors, 2023
For the existing visual–inertial SLAM algorithm, when the robot is moving at a constant speed or purely rotating and encounters scenes with insufficient visual features, problems of low accuracy and poor robustness arise.
Gang Peng   +6 more
doaj   +1 more source

Continuous-Time Spline Visual-Inertial Odometry

open access: yes2022 International Conference on Robotics and Automation (ICRA), 2022
We propose a continuous-time spline-based formulation for visual-inertial odometry (VIO). Specifically, we model the poses as a cubic spline, whose temporal derivatives are used to synthesize linear acceleration and angular velocity, which are compared to the measurements from the inertial measurement unit (IMU) for optimal state estimation. The spline
Jiawei Mo, Junaed Sattar
openaire   +2 more sources

Real-Time Localization and Mapping Utilizing Multi-Sensor Fusion and Visual–IMU–Wheel Odometry for Agricultural Robots in Unstructured, Dynamic and GPS-Denied Greenhouse Environments

open access: yesAgronomy, 2022
Autonomous navigation in greenhouses requires agricultural robots to localize and generate a globally consistent map of surroundings in real-time. However, accurate and robust localization and mapping are still challenging for agricultural robots due to ...
Yaxuan Yan   +4 more
doaj   +1 more source

Visual 3-D SLAM from UAVs [PDF]

open access: yes, 2009
The aim of the paper is to present, test and discuss the implementation of Visual SLAM techniques to images taken from Unmanned Aerial Vehicles (UAVs) outdoors, in partially structured environments.
Campoy Cervera, Pascual   +14 more
core   +2 more sources

Visual-LiDAR Odometry Aided by Reduced IMU

open access: yesISPRS International Journal of Geo-Information, 2016
This paper proposes a method for combining stereo visual odometry, Light Detection And Ranging (LiDAR) odometry and reduced Inertial Measurement Unit (IMU) including two horizontal accelerometers and one vertical gyro.
Yashar Balazadegan Sarvrood   +2 more
doaj   +1 more source

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