This work presents KMVO, a ground plane based visual odometry that utilizes the vehicle’s kinematic model to improve accuracy and robustness. Instead of solving a generic image alignment problem, the motion parameters of a differential drive vehicle can be directly estimated from RGB- D image data. In addition, a method for outlier rejection is presented that can deal with large percentages of outliers. The system is designed to run in real time on a single thread of a mobile CPU. The results of the proposed method are compared to other publicly available visual odometry and SLAM methods on a set of nine real world image sequences of different indoor environments.
@inproceedings{JordanECMR2017, title = {Kinematic Model based Visual Odometry for Differential Drive Vehicles}, author = {Jordan, Julian and Zell, Andreas}, booktitle = {IEEE 8th European Conference on Mobile Robots (ECMR)}, year = {2017}, address = {ENSTA ParisTech, Université Paris-Saclay, France}, month = sept, abstract = {This work presents KMVO, a ground plane based visual odometry that utilizes the vehicle’s kinematic model to improve accuracy and robustness. Instead of solving a generic image alignment problem, the motion parameters of a differential drive vehicle can be directly estimated from RGB- D image data. In addition, a method for outlier rejection is presented that can deal with large percentages of outliers. The system is designed to run in real time on a single thread of a mobile CPU. The results of the proposed method are compared to other publicly available visual odometry and SLAM methods on a set of nine real world image sequences of different indoor environments.}, days = {6-8}, note = {}, }