activity
20152019
most citedTraining a Convolutional Neural Network for Appearance-Invariant Place Recognition

52 citations · 84 across the 3 of their papers we have counts for

collaborators

5 papers

cs.RO2019★ 31 cited

Automatic Multi-Sensor Extrinsic Calibration for Mobile Robots

David Zuñiga-Noël, Jose-Raul Ruiz-Sarmiento, Ruben Gomez-Ojeda +1

In order to fuse measurements from multiple sensors mounted on a mobile robot, it is needed to express them in a common reference system through their relative spatial transformati…

cs.CV2018★ 1 cited

Geometric-based Line Segment Tracking for HDR Stereo Sequences

Ruben Gomez-Ojeda, Javier Gonzalez-Jimenez

In this work, we propose a purely geometrical approach for the robust matching of line segments for challenging stereo streams with severe illumination changes or High Dynamic Rang…

cs.CV2017

Learning-based Image Enhancement for Visual Odometry in Challenging HDR Environments

Ruben Gomez-Ojeda, Zichao Zhang, Javier Gonzalez-Jimenez +1

One of the main open challenges in visual odometry (VO) is the robustness to difficult illumination conditions or high dynamic range (HDR) environments. The main difficulties in th…

cs.CV2017

PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

Ruben Gomez-Ojeda, David Zuñiga-Noël, Francisco-Angel Moreno +2

Traditional approaches to stereo visual SLAM rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it i…

cs.CV2015★ 52 cited

Training a Convolutional Neural Network for Appearance-Invariant Place Recognition

Ruben Gomez-Ojeda, Manuel Lopez-Antequera, Nicolai Petkov +1

Place recognition is one of the most challenging problems in computer vision, and has become a key part in mobile robotics and autonomous driving applications for performing loop c…