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robots-9

Visual Odometry

3260 papers • 126 benchmarks • 313 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

(Image credit: Papersgraph)

Benchmarks

These leaderboards are used to track progress in visual-odometry-18

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find visual-odometry-18 models and implementations

minxuanjun/basalt_class

2 papers 21

Datasets

KITTI

TUM RGB-D

Virtual KITTI

TartanAir

Event-Camera Dataset

Event-Camera Dataset

Virtual KITTI 2

Virtual KITTI 2

Subtasks

Face Anti-Spoofing Monocular Visual Odometry

Most implemented papers

The Double Sphere Camera Model

D. Cremers, V. Usenko, Nikolaus Demmel•Mon Jul 23 2018

This paper proposes the Double Sphere camera model, which well fits with large field-of-view lenses, is computationally inexpensive and has a closed-form inverse, and is evaluated using a calibration dataset with several different lenses.

102

Content

Introduction Benchmarks Datasets Subtasks Libraries Papers

New College

New College

4Seasons

TUM monoVO

EndoSLAM

0

DeepVO: Towards end-to-end visual odometry with deep Recurrent Convolutional Neural Networks

R. Clark, A. Trigoni, Sen Wang, Hongkai Wen•Sat Jan 14 2017

Extensive experiments on the KITTI VO dataset show competitive performance to state-of-the-art methods, verifying that the end-to-end Deep Learning technique can be a viable complement to the traditional VO systems.

864 0

The TUM VI Benchmark for Evaluating Visual-Inertial Odometry

D. Cremers, V. Usenko, Nikolaus Demmel, David Schubert, Thore Goll, J. Stückler•Mon Apr 16 2018

The TUM VI benchmark is proposed, a novel dataset with a diverse set of sequences in different scenes for evaluatingVI odometry, which provides camera images with 1024×1024 resolution at 20 Hz, high dynamic range and photometric calibration, and evaluates state-of-the-art VI odometry approaches on this dataset.

432 0

A General Optimization-based Framework for Local Odometry Estimation with Multiple Sensors

S. Shen, Tong Qin, Jie Pan, Shaozu Cao•Thu Jan 10 2019

This paper proposes a sensor fusion framework to fuse local states with global sensors, which achieves locally accurate and globally drift-free pose estimation and highlights that the system is a general framework, which can easily fuse various global sensors in a unified pose graph optimization.

380 0

Towards Better Generalization: Joint Depth-Pose Learning Without PoseNet

Wang Zhao, Shaohui Liu, Yezhi Shu, Yong-Jin Liu•Thu Apr 02 2020

A novel system that explicitly disentangles scale from the network estimation, which achieves state-of-the-art results among self-supervised learning-based methods on KITTI Odometry and NYUv2 dataset and presents some interesting findings on the limitation of PoseNet-based relative pose estimation methods in terms of generalization ability.

169 0

PL-SLAM: A Stereo SLAM System Through the Combination of Points and Line Segments

D. Scaramuzza, Ruben Gomez-Ojeda, F. Moreno, David Zuñiga-Noël, J. Gonzalez-Jimenez•Thu May 25 2017

PL-SLAM is proposed, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image.

504 0

Direct Sparse Odometry

D. Cremers, V. Koltun, Jakob J. Engel•Fri Jul 08 2016

The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.

2802 0

Stereo Relative Pose from Line and Point Feature Triplets

V. Lempitsky, Yinqiang Zheng, Alexander Vakhitov•Fri Sep 07 2018

This work presents two minimal solvers for the stereo relative pose problem, specifically the case when a minimal set consists of three point or line features and each of them has three known projections on two stereo cameras.

15 0

Reducing drift in visual odometry by inferring sun direction using a Bayesian Convolutional Neural Network

Jonathan Kelly, Valentin Peretroukhin, Lee Clement•Mon Sep 19 2016

A Bayesian sun detection model that infers a three-dimensional sun direction vector from a single RGB image and computes a principled uncertainty associated with each prediction, using a Monte Carlo dropout scheme is presented.

28 0

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Visual Odometry | State-of-the-Art