Search
Publication Authors

Prof. Dr. Didier Stricker

Dr. Alain Pagani

Dr. Gerd Reis

Eric Thil

Keonna Cunningham

Dr. Oliver Wasenmüller

Dr. Muhammad Zeshan Afzal

Dr. Gabriele Bleser

Dr. Muhammad Jameel Nawaz Malik
Dr. Bruno Mirbach

Dr. Jason Raphael Rambach

Dr. Nadia Robertini

Dr. René Schuster

Dr. Bertram Taetz

Ahmed Aboukhadra

Sk Aziz Ali

Mhd Rashed Al Koutayni

Yuriy Anisimov

Jilliam Maria Diaz Barros

Ramy Battrawy
Hammad Butt

Mahdi Chamseddine

Steve Dias da Cruz
Fangwen Shu

Torben Fetzer

Ahmet Firintepe
Sophie Folawiyo

David Michael Fürst

Christiano Couto Gava

Tewodros Amberbir Habtegebrial
Simon Häring

Khurram Azeem Hashmi
Henri Hoyez

Jigyasa Singh Katrolia

Andreas Kölsch
Onorina Kovalenko

Stephan Krauß
Paul Lesur

Michael Lorenz

Dr. Markus Miezal

Mina Ameli

Nareg Minaskan Karabid

Mohammad Minouei

Pramod Murthy

Mathias Musahl

Peter Neigel

Manthan Pancholi
Mariia Podguzova

Praveen Nathan
Qinzhuan Qian
Rishav
Marcel Rogge
María Alejandra Sánchez Marín
Dr. Kripasindhu Sarkar

Alexander Schäfer

Pascal Schneider

Mohamed Selim

Tahira Shehzadi
Lukas Stefan Staecker

Yongzhi Su

Xiaoying Tan
Christian Witte

Yaxu Xie

Vemburaj Yadav

Dr. Vladislav Golyanik

Dr. Aditya Tewari

André Luiz Brandão
Publication Archive
New title
- ActivityPlus
- AlterEgo
- AR-Handbook
- ARVIDA
- Auroras
- AVILUSplus
- Be-greifen
- Body Analyzer
- CAPTURE
- COGNITO
- DAKARA
- Density
- DYNAMICS
- EASY-IMP
- Eyes Of Things
- iACT
- IMCVO
- IVMT
- LARA
- LiSA
- Marmorbild
- Micro-Dress
- Odysseus Studio
- On Eye
- OrcaM
- PAMAP
- PROWILAN
- ServiceFactory
- STREET3D
- SUDPLAN
- SwarmTrack
- TuBUs-Pro
- VIDETE
- VIDP
- VisIMon
- VISTRA
- You in 3D
DeLiO: Decoupled LiDAR Odometry
DeLiO: Decoupled LiDAR Odometry
Queens Maria Thomas, Oliver Wasenmüller, Didier Stricker
IEEE Intelligent Vehicles Symposium. IEEE Intelligent Vehicles Symposium (IV-2019) IEEE 2019 .
- Abstract:
- Most LiDAR odometry algorithms estimate the transformation between two consecutive frames by estimating the rotation and translation in an intervening fashion. In this paper, we propose our Decoupled LiDAR Odometry (DeLiO), which -- for the first time -- decouples the rotation estimation completely from the translation estimation. In particular, the rotation is estimated by extracting the surface normals from the input point clouds and tracking their characteristic pattern on a unit sphere. Using this rotation the point clouds are unrotated so that the underlying transformation is pure translation, which can be easily estimated using a line cloud approach. An evaluation is performed on the KITTI dataset and the results are compared against state-of-the-art algorithms.