TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

Discrimination of active dynamic objects in stereo-based visual SLAM

Tutkimustuotosvertaisarvioitu

Yksityiskohdat

AlkuperäiskieliEnglanti
OtsikkoElectronic Imaging
AlaotsikkoImage Processing: Algorithms and Systems XVI
KustantajaSociety for Imaging Science and Technology
Sivumäärä6
DOI - pysyväislinkit
TilaJulkaistu - 2018
OKM-julkaisutyyppiA4 Artikkeli konferenssijulkaisussa
TapahtumaIS&T International Symposium on Electronic Imaging -
Kesto: 28 tammikuuta 20182 helmikuuta 2018

Julkaisusarja

Nimi
ISSN (elektroninen)2470-1173

Conference

ConferenceIS&T International Symposium on Electronic Imaging
Ajanjakso28/01/182/02/18

Tiivistelmä

Over the years, the problem of simultaneous localization and mapping have been substantially studied. Effective and robust techniques have been developed for mapping and localizing in an unknown environment in real-time. However, the bulk of the work presumes that the environment under observation is composed of static objects. In this study, we propose an approach aimed at localizing and mapping an environment irrespective of the motion of the objects in the scene. A hard threshold based Iterative Closest Point algorithm is used to compute transformations between point clouds that are obtained from dense stereo matching. The dynamic entities along with system noise are identified and isolated in the form of outliers of the data correspondence step. A confidence metric is defined that helps in identifying and transitioning a 3D point from static to dynamic and vice versa. The results are then verified in a 2D domain with the aid of a modified Gaussian Mixture Model based motion estimation. The dynamic objects are segmented in 3D and 2D domains for any possible analysis and decision making. The results demonstrate that the proposed approach effectively eliminates noise and isolates the dynamic objects during the mapping of the environment.