VINS-MKF: A Tightly-Coupled Multi-Keyframe Visual-Inertial Odometry for Accurate and Robust State Estimation [PDF]
Sensors, 2018 State estimation is crucial for robot autonomy, visual odometry (VO) has received significant attention in the robotics field because it can provide accurate state estimation.
Chaofan Zhang +4 more
doaj +2 more sources
RISE-VIO: Robust Initialization and Targeted Pose Robustification for INS-Centric Visual–Inertial Odometry Under Degraded Visual Conditions [PDF]
SensorsFeature-based visual–inertial odometry (VIO) often suffers from initialization failures and tracking drift under degraded visual conditions, such as low-texture regions, abrupt illumination changes, and scenes with a high ratio of dynamic correspondences.
Xiaowei Xu +3 more
doaj +2 more sources
RGB-Based Visual–Inertial Odometry via Knowledge Distillation from Self-Supervised Depth Estimation with Foundation Models [PDF]
SensorsAutonomous driving represents a transformative advancement with the potential to significantly impact daily mobility, including enabling independent vehicle operation for individuals with visual disabilities.
Jimin Song, Sang Jun Lee
doaj +2 more sources
LRPL-VIO: A Lightweight and Robust Visual–Inertial Odometry with Point and Line Features [PDF]
SensorsVisual-inertial odometry (VIO) algorithms, fusing various features such as points and lines, are able to improve their performance in challenging scenes while the running time severely increases.
Feixiang Zheng +4 more
doaj +2 more sources
CVIWM: A Tightly Coupled State Estimation Method for Poultry House Inspection Robots in Structurally Degraded Environments [PDF]
AnimalsAccurate positioning is essential for inspection robots in caged chicken houses, where long straight corridors, sparse textures, and repetitive structures challenge conventional methods.
Hongfeng Deng +4 more
doaj +2 more sources
F-LVINS: Flexible Lidar-Visual-Inertial Odometry Systems
IEEE Access, 2023 The development of a new system called Flexible Lidar-Visual-Inertial Odometry (F-LVINS) offers improved localization accuracy even in challenging environments.
Xiang-Shi Tang, Teng-Hu Cheng
doaj +1 more source
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
doaj +1 more source
Monocular Visual Inertial Direct SLAM with Robust Scale Estimation for Ground Robots/Vehicles
Robotics, 2021 In this paper, we present a novel method for visual-inertial odometry for land vehicles. Our technique is robust to unintended, but unavoidable bumps, encountered when an off-road land vehicle traverses over potholes, speed-bumps or general change in ...
Bismaya Sahoo +2 more
doaj +1 more source
Learned Inertial Odometry for Autonomous Drone Racing [PDF]
, 2023 Inertial odometry is an attractive solution to the problem of state estimation for agile quadrotor flight. It is inexpensive, lightweight, and it is not affected by perceptual degradation.
Cioffi, Giovanni +3 more
core +2 more sources
ROBUST VISUAL-INERTIAL ODOMETRY IN DYNAMIC ENVIRONMENTS USING SEMANTIC SEGMENTATION FOR FEATURE SELECTION [PDF]
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2020 Camera based navigation in dynamic environments with high content of moving objects is challenging. Keypoint-based localization methods need to reliably reject features that do not belong to the static background.
P. Irmisch, D. Baumbach, I. Ernst
doaj +1 more source