2D SLAM Correction Prediction in Large Scale Urban Environments - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2018

2D SLAM Correction Prediction in Large Scale Urban Environments


Simultaneous Localization And Mapping (SLAM) is one of the major bricks needed to build truly autonomous mobile robots. The probabilistic formulation of SLAM is based on two models: the motion model and the observation model. In practice, these models, together with the SLAM map representation, do not model perfectly the robot's real dynamics, the sensor measurement errors and the environment. Consequently, systematic errors affect SLAM estimations. In this paper, we propose two approaches to predict corrections to be applied to SLAM estimations. Both are based on the Ensemble Multilayer Perceptron model. The first approach uses successive estimated poses to predict the errors, with no assumptions on the underlying SLAM process or sensor used. The second method is specific to 2D likelihood SLAM approaches, thus, the likelihood distributions are used to predict the corrections, making this second approach independent of the sensor used. We also build a hybrid correction module based on successive estimated poses and the likelihood distributions. The validity of both approaches is evaluated through two experiments using different evaluation metrics and sensor configurations.
Fichier principal
Vignette du fichier
SLAM Correction Estimation - ICRA2018 v3Fin.pdf (1.14 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-01829091 , version 1 (03-07-2018)


  • HAL Id : hal-01829091 , version 1


Zayed Alsayed, Guillaume Bresson, Anne Verroust-Blondet, Fawzi Nashashibi. 2D SLAM Correction Prediction in Large Scale Urban Environments. ICRA 2018 - International Conference on Robotics and Automation 2018, May 2018, Brisbane, Australia. ⟨hal-01829091⟩


163 View
623 Download


Gmail Mastodon Facebook X LinkedIn More