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Discrimination of active dynamic objects in stereo-based visual SLAM

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review


Original languageEnglish
Title of host publicationElectronic Imaging
Subtitle of host publicationImage Processing: Algorithms and Systems XVI
PublisherSociety for Imaging Science and Technology
Number of pages6
Publication statusPublished - 2018
Publication typeA4 Article in a conference publication
EventIS&T International Symposium on Electronic Imaging -
Duration: 28 Jan 20182 Feb 2018

Publication series

ISSN (Electronic)2470-1173


ConferenceIS&T International Symposium on Electronic Imaging


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.