José-Raúl Ruiz-Sarmiento — accepted AI-conference papers

2023

GND-LO: Ground Decoupled 3D Lidar Odometry Based on Planar Patches

Andres Galeote-Luque, José-Raúl Ruiz-Sarmiento, Javier González Jiménez

RA-L 2023

We present a new fast Ground Decoupled 3D Lidar Odometry (GND-LO) method. The particularity of GND-LO is that it takes advantage of the distinct spatial layout found in urban settings to efficiently recover the lidar movement in a decoupled manner. For that, the input scans are reduced to a set of p

Cited by 3 Source ↗Scholar ↗

2019

Automatic Multi-Sensor Extrinsic Calibration For Mobile Robots

David Zuñiga-Noël, José-Raúl Ruiz-Sarmiento, Ruben Gomez-Ojeda, Javier González Jiménez

RA-L 2019

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 transformations. In this letter, we present a method to estimate the full 6-DoF extrinsic calibration parameters of multiple heterog

Cited by 25 Source ↗code ↗Scholar ↗