TwistSLAM: Constrained SLAM in Dynamic Environment - Archive ouverte HAL Access content directly
Journal Articles IEEE Robotics and Automation Letters Year : 2022

TwistSLAM: Constrained SLAM in Dynamic Environment

(1, 2) , (2) , (1) , (1)
1
2

Abstract

Classical visual simultaneous localization and mapping (SLAM) algorithms usually assume the environment to be rigid. This assumption limits the applicability of those algorithms as they are unable to accurately estimate the camera poses and world structure in real life scenes containing moving objects (e.g. cars, bikes, pedestrians, etc.). To tackle this issue, we propose TwistSLAM: a semantic, dynamic and stereo SLAM system that can track dynamic objects in the environment. Our algorithm creates clusters of points according to their semantic class. Thanks to the definition of inter-cluster constraints modeled by mechanical joints (function of the semantic class), a novel constrained bundle adjustment is then able to jointly estimate both poses and velocities of moving objects along with the classical world structure and camera trajectory. We evaluate our approach on several sequences from the public KITTI dataset and demonstrate quantitatively that it improves camera and object tracking compared to state-of-the-art approaches.

Keywords

Fichier principal
Vignette du fichier
TwistSLAM_accepted_RAL.pdf (1.74 Mo) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-03676527 , version 1 (24-05-2022)

Identifiers

  • HAL Id : hal-03676527 , version 1

Cite

Mathieu Gonzalez, Eric Marchand, Amine Kacete, Jerome Royan. TwistSLAM: Constrained SLAM in Dynamic Environment. IEEE Robotics and Automation Letters, 2022, 7 (3), pp.6846-6853. ⟨hal-03676527⟩
52 View
20 Download

Share

Gmail Facebook Twitter LinkedIn More