NDLI: Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization

Content Provider	IEEE Xplore Digital Library
Author	Indelman, V. Nelson, E. Michael, N. Dellaert, F.
Copyright Year	2014
Description	Author affiliation: Robot. Inst., Carnegie Mellon, Pittsburgh, PA, USA (Nelson, E.; Michael, N.) \|\| Inst. for Robot. & Intell. Machines (IRIM), Georgia Inst. of Technol., Atlanta, GA, USA (Indelman, V.; Dellaert, F.)
Abstract	This paper presents a novel approach for multirobot pose graph localization and data association without requiring prior knowledge about the initial relative poses of the robots. Without a common reference frame, the robots can only share observations of interesting parts of the environment, and trying to match between observations from different robots will result in many outlier correspondences. Our approach is based on the following key observation: while each multi-robot correspondence can be used in conjunction with the local robot estimated trajectories, to calculate the transformation between the robot reference frames, only the inlier correspondences will be similar to each other. Using this concept, we develop an expectation-maximization (EM) approach to efficiently infer the robot initial relative poses and solve the multi-robot data association problem. Once this transformation between the robot reference frames is estimated with sufficient measure of confidence, we show that a similar EM formulation can be used to solve also the full multi-robot pose graph problem with unknown multi-robot data association. We evaluate the performance of the developed approach both in a statistical synthetic-environment study and in a real-data experiment, demonstrating its robustness to high percentage of outliers.
Starting Page	593
Ending Page	600
File Size	3503321
Page Count	8
File Format	PDF
ISBN	9781479936854
DOI	10.1109/ICRA.2014.6906915
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Robot kinematics Trajectory Optimization Robustness Simultaneous localization and mapping
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization.

Consistent multi-robot decentralized SLAM with unknown initial positions

Toward robust linear SLAM

Multiple Relative Pose Graphs for Robust Cooperative Mapping

A pose graph based visual SLAM algorithm for robot pose estimation

Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Fully distributed scalable smoothing and mapping with robust multi-robot data association

Multi-robot Simultaneous Localization and Mapping using Particle Filters

Multi-robot simultaneous localization and mapping using particle filters.

Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization

Similar Documents

Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization.

Consistent multi-robot decentralized SLAM with unknown initial positions

Toward robust linear SLAM

Multiple Relative Pose Graphs for Robust Cooperative Mapping

A pose graph based visual SLAM algorithm for robot pose estimation

Distributed real-time cooperative localization and mapping using an uncertainty-aware expectation maximization approach

Fully distributed scalable smoothing and mapping with robust multi-robot data association

Multi-robot Simultaneous Localization and Mapping using Particle Filters

Multi-robot simultaneous localization and mapping using particle filters.

Multi-robot pose graph localization and data association from unknown initial relative poses via expectation maximization