Results 91 to 100 of about 3,501 (181)

Deep Event Visual Odometry

2024 International Conference on 3D Vision (3DV)
Event cameras offer the exciting possibility of tracking the camera's pose during high-speed motion and in adverse lighting conditions. Despite this promise, existing event-based monocular visual odometry (VO) approaches demonstrate limited performance on recent benchmarks.
Simon Klenk, Marvin Motzet, Lukas Koestler, Daniel Cremers   +3 more
openaire   +2 more sources

Survey of Deep Learning-Based Methods for FMCW Radar Odometry and Ego-Localization

Applied Sciences
This paper provides an in-depth review of deep learning techniques to address the challenges of odometry and global ego-localization using frequency modulated continuous wave (FMCW) radar sensors.
Marvin Brune, Tobias Meisen, André Pomp
doaj   +1 more source

An evaluation of CFEAR Radar Odometry

CoRR
Uppdated with results from test set in ...
Daniel Adolfsson, Maximilian Hilger
openaire   +2 more sources

Accurate parameter estimation of systematic odometry errors for two-wheel differential mobile robots

Journal of Measurement Science and Instrumentation, 2012
Odometry using incremental wheel encoder sensors provides the relative robot pose estimation.However,the odometry suffers from the accumulation of kinematic modeling errors of wheels as the robot’s travel distance increases.Therefore,the systematic ...
Changbae Jung, Woojin Chung
doaj  

Robust Visual Odometry

, 2021
openaire   +2 more sources

Large-Scale Drift-Resilient Localization via Multi-Sensor Fusion and Topological Map Matching. [PDF]

Sensors (Basel)
Yang X, Shao C, Hou P, Yan J, Fu W.
europepmc   +1 more source

Deskewed LiDAR Odometry for Quadruped Robots in Environments with Varying Elevation. [PDF]

Sensors (Basel)
Han E, Lee H.
europepmc   +1 more source

IFA-ICP: A Low-Complexity and Image Feature-Assisted Iterative Closest Point (ICP) Scheme for Odometry Estimation in SLAM, and Its FPGA-Based Hardware Accelerator Design. [PDF]

Sensors (Basel)
Li JE, Hwang YT.
europepmc   +1 more source

Navigation Error Characteristics of LIO-, VIO-, and RIMU-Assisted INS/GNSS Multi-Sensor Fusion Schemes in a GNSS-Denied Environment. [PDF]

Sensors (Basel)
Chiang KW, Tsai S, Huang CH, Lu YE, Srinara S, Tsai ML, El-Sheimy N, Ai M.   +7 more
europepmc   +1 more source

DB-LIO: Database-Driven LiDAR-Inertial Odometry for Memory-Bounded Persistent Mapping. [PDF]

Sensors (Basel)
Kim HH, Kang HH, Noh DH, Lee HM.
europepmc   +1 more source