ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual–Inertial, and Multimap SLAM [PDF]
IEEE Transactions on robotics, 2020 This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models.
C. Campos +4 more
semanticscholar +1 more source
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
On-Manifold Preintegration for Real-Time Visual--Inertial Odometry [PDF]
IEEE Transactions on robotics, 2015 Current approaches for visual--inertial odometry (VIO) are able to attain highly accurate state estimation via nonlinear optimization. However, real-time optimization quickly becomes infeasible as the trajectory grows over time; this problem is further ...
Christian Forster +3 more
semanticscholar +1 more source
DM-VIO: Delayed Marginalization Visual-Inertial Odometry [PDF]
IEEE Robotics and Automation Letters, 2022 We present DM-VIO, a monocular visual-inertial odometry system based on two novel techniques called delayed marginalization and pose graph bundle adjustment. DM-VIO performs photometric bundle adjustment with a dynamic weight for visual residuals.
L. Stumberg, D. Cremers
semanticscholar +1 more source
Unsupervised Depth Completion From Visual Inertial Odometry [PDF]
IEEE Robotics and Automation Letters, 2020 We describe a method to infer dense depth from camera motion and sparse depth as estimated using a visual-inertial odometry system. Unlike other scenarios using point clouds from lidar or structured light sensors, we have few hundreds to few thousand points, insufficient to inform the topology of the scene.
Alex Wong +3 more
openaire +2 more sources
VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator [PDF]
IEEE Transactions on robotics, 2017 One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation.
Tong Qin, Peiliang Li, S. Shen
semanticscholar +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
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Finding Your Way Back: Comparing Path Odometry Algorithms for Assisted Return. [PDF]
, 2021 We present a comparative analysis of inertial-based odometry algorithms for the purpose of assisted return. An assisted return system facilitates backtracking of a path previously taken, and can be particularly useful for blind pedestrians.
Elyasi, Fatemeh +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