Visual Odometry
96 papers with code • 0 benchmarks • 21 datasets
Visual Odometry is an important area of information fusion in which the central aim is to estimate the pose of a robot using data collected by visual sensors.
Source: Bi-objective Optimization for Robust RGB-D Visual Odometry
Benchmarks
These leaderboards are used to track progress in Visual Odometry
Libraries
Use these libraries to find Visual Odometry models and implementationsDatasets
KITTI
TUM RGB-D
Virtual KITTI
TartanAir
Event-Camera Dataset
Virtual KITTI 2
4Seasons
TUM monoVO
EndoSLAM
Latest papers with no code
SPOT: Point Cloud Based Stereo Visual Place Recognition for Similar and Opposing Viewpoints
Recognizing places from an opposing viewpoint during a return trip is a common experience for human drivers.
Salient Sparse Visual Odometry With Pose-Only Supervision
Visual Odometry (VO) is vital for the navigation of autonomous systems, providing accurate position and orientation estimates at reasonable costs.
A Comparative Analysis of Visual Odometry in Virtual and Real-World Railways Environments
To illustrate the advantages of employing graphic simulation for early-stage testing of perception tasks in the railway domain, this paper presents a comparative analysis of the performance of a SLAM algorithm applied both in a virtual synthetic environment and a real-world scenario.
An Accurate and Real-time Relative Pose Estimation from Triple Point-line Images by Decoupling Rotation and Translation
First, a high-precision rotation estimation method based on normal vector coplanarity constraints that consider the uncertainty of observations is proposed, which can be solved by Levenberg-Marquardt (LM) algorithm efficiently.
The POLAR Traverse Dataset: A Dataset of Stereo Camera Images Simulating Traverses across Lunar Polar Terrain under Extreme Lighting Conditions
We present the POLAR Traverse Dataset: a dataset of high-fidelity stereo pair images of lunar-like terrain under polar lighting conditions designed to simulate a straight-line traverse.
Efficient Domain Adaptation for Endoscopic Visual Odometry
In this work, an efficient neural style transfer framework for endoscopic visual odometry is proposed, which compresses the time from pre-operative planning to testing phase to less than five minutes.
Secure Navigation using Landmark-based Localization in a GPS-denied Environment
In modern battlefield scenarios, the reliance on GPS for navigation can be a critical vulnerability.
Landmark-based Localization using Stereo Vision and Deep Learning in GPS-Denied Battlefield Environment
The proposed method utilizes a customcalibrated stereo vision camera for distance estimation and the YOLOv8s model, which is trained and fine-tuned with our real-world dataset for landmark recognition.
Motion Consistency Loss for Monocular Visual Odometry with Attention-Based Deep Learning
Deep learning algorithms have driven expressive progress in many complex tasks.
LEAP-VO: Long-term Effective Any Point Tracking for Visual Odometry
Visual odometry estimates the motion of a moving camera based on visual input.
