UZH-Logo

Maintenance Infos

Multi-body motion estimation from monocular vehicle-mounted cameras


Sabzevari, Reza; Scaramuzza, Davide (2016). Multi-body motion estimation from monocular vehicle-mounted cameras. IEEE Transactions on Robotics, 32(3):638-651.

Abstract

This paper addresses the problem of simultaneous estimation of the vehicle ego-motion and motions of multiple moving objects in the scene—called eoru-motions—through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g. Visual Odometry and SLAM) can only estimate the ego-motion of the vehicle. The capability of robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego-motion. First, the framework for general unconstrained motion is introduced and then, it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego-motion is segmented and, then, several hypotheses are generated for the eoru-motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (KITTI dataset) including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as the Multi-Body Structure from Motion in the computer vision community.

Abstract

This paper addresses the problem of simultaneous estimation of the vehicle ego-motion and motions of multiple moving objects in the scene—called eoru-motions—through a monocular vehicle-mounted camera. Localization of multiple moving objects and estimation of their motions is crucial for autonomous vehicles. Conventional localization and mapping techniques (e.g. Visual Odometry and SLAM) can only estimate the ego-motion of the vehicle. The capability of robot localization pipeline to deal with multiple motions has not been widely investigated in the literature. We present a theoretical framework for robust estimation of multiple relative motions in addition to the camera ego-motion. First, the framework for general unconstrained motion is introduced and then, it is adapted to exploit the vehicle kinematic constraints to increase efficiency. The method is based on projective factorization of the multiple-trajectory matrix. First, the ego-motion is segmented and, then, several hypotheses are generated for the eoru-motions. All the hypotheses are evaluated and the one with the smallest reprojection error is selected. The proposed framework does not need any a priori knowledge of the number of motions and is robust to noisy image measurements. The method with constrained motion model is evaluated on a popular street-level image dataset collected in urban environments (KITTI dataset) including several relative ego-motion and eoru-motion scenarios. A benchmark dataset (Hopkins 155) is used to evaluate this method with general motion model. The results are compared with those of the state-of-the-art methods considering a similar problem, referred to as the Multi-Body Structure from Motion in the computer vision community.

Altmetrics

Downloads

2 downloads since deposited on 12 Aug 2016
2 downloads since 12 months
Detailed statistics

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Uncontrolled Keywords:SLAM (robots);cameras;computer vision;image segmentation;matrix decomposition;motion estimation;object detection;camera ego motion;computer vision community;constrained motion model;eoru motions;monocular vehicle-mounted cameras;multibody motion estimation;multibody structure from motion;multiple moving object localization;noisy image measurements;projective multiple-trajectory matrix factorization;reprojection error;robot localization pipeline;simultaneous multiple moving object-vehicle ego motion estimation;street-level image dataset;unconstrained motion;urban environments;vehicle kinematic constraints;Cameras;Computer vision;Estimation;Image segmentation;Motion segmentation;Tracking;Vehicles;Computer vision;eoru-motion estimation;multi-body structure from motion;simultaneous localization and mapping (SLAM)
Language:English
Date:June 2016
Deposited On:12 Aug 2016 06:24
Last Modified:13 Aug 2016 03:07
Publisher:Institute of Electrical and Electronics Engineers
ISSN:1552-3098
Additional Information:© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/TRO.2016.2552548
Related URLs:http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7484935&isnumber=7484799 (Publisher)
Other Identification Number:merlin-id:13371

Download

[img]
Preview
Content: Accepted Version
Filetype: PDF
Size: 8MB
View at publisher

TrendTerms

TrendTerms displays relevant terms of the abstract of this publication and related documents on a map. The terms and their relations were extracted from ZORA using word statistics. Their timelines are taken from ZORA as well. The bubble size of a term is proportional to the number of documents where the term occurs. Red, orange, yellow and green colors are used for terms that occur in the current document; red indicates high interlinkedness of a term with other terms, orange, yellow and green decreasing interlinkedness. Blue is used for terms that have a relation with the terms in this document, but occur in other documents.
You can navigate and zoom the map. Mouse-hovering a term displays its timeline, clicking it yields the associated documents.

Author Collaborations