research-article

Robust and Scalable Indoor Robot Localization Based on Fusion of Infrastructure Camera Feeds and On-Board Sensors

Authors:

Raghu Krishnapuram,

Mukunda Bharatheesha,

Bharadwaj Amrutur,

Suresh SundaramAuthors Info & Claims

AIR '23: Proceedings of the 2023 6th International Conference on Advances in Robotics

Article No.: 41, Pages 1 - 7

https://doi.org/10.1145/3610419.3610460

Published: 02 November 2023 Publication History

Abstract

In this paper, we propose a method to quantify the spatially-varying uncertainty associated with external-camera-based pose estimates of autonomous mobile robots in an indoor setting. We build an observation model for the camera based on an estimate of an upper bound on the uncertainty and demonstrate through experiments how it can be used to fuse information from an on-board lidar to arrive at accurate localization of the robot in feature-poor or changing environments.

References

[1]

Adrian Canedo-Rodriguez, Víctor Álvarez Santos, Carlos Regueiro, Roberto Iglesias Rodriguez, S. Barro, and Jesus Presedo. 2015. Particle filter robot localisation through robust fusion of laser, WiFi, compass, and a network of external cameras. Information Fusion 27 (04 2015). https://doi.org/10.1016/j.inffus.2015.03.006

Digital Library

[2]

Punarjay Chakravarty and Ray A. Jarvis. 2009. External cameras and a mobile robot: A collaborative surveillance system. In IEEE International Conference on Robotics and Automation.

[3]

Alessandro Faralli, Niko Giovannini, Simone Nardi, and Lucia Pallottino. 2016. Indoor Real-Time Localisation for Multiple Autonomous Vehicles Fusing Vision, Odometry and IMU Data. In Modelling and Simulation for Autonomous Systems, Jan Hodicky (Ed.). Springer International Publishing, Cham, 288–297.

[4]

Dieter Fox. 2001. KLD-Sampling: Adaptive Particle Filters. In Advances in Neural Information Processing Systems, T. Dietterich, S. Becker, and Z. Ghahramani (Eds.). Vol. 14. MIT Press.

[5]

Xiao-Shan Gao, Xiao-Rong Hou, Jianliang Tang, and Hang-Fei Cheng. 2003. Complete solution classification for the perspective-three-point problem. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 8 (2003), 930–943. https://doi.org/10.1109/TPAMI.2003.1217599

Digital Library

[6]

Armin Hornung, Stefan Oßwald, Daniel Maier, and Maren Bennewitz. 2014. Monte Carlo Localization for Humanoid Robot Navigation in Complex Indoor Environments. International Journal of Humanoid Robotics 11 (06 2014). https://doi.org/10.1142/S0219843614410023

[7]

Patricia Javierre, Biel Piero E. Alvarado Vasquez, and Paloma de la Puente. 2019. Particle Filter Localization Using Visual Markers Based Omnidirectional Vision and a Laser Sensor. 2019 Third IEEE International Conference on Robotic Computing (IRC) (2019), 246–249.

[8]

E. Kruse and F.M. Wahl. 1998. Camera-based monitoring system for mobile robot guidance. In Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190), Vol. 2. 1248–1253 vol.2. https://doi.org/10.1109/IROS.1998.727470

[9]

Emin Kuscu and Aaron Rasheed Rababaah. 2013. MOBILE ROBOT LOCALIZATION VIA EFFICIENT CALIBRATION TECHNIQUE OF A FIXED REMOTE CAMERA.

[10]

Vincent Lepetit, Francesc Moreno-Noguer, and Pascal Fua. 2009. EPnP: An accurate O(n) solution to the PnP problem. International Journal of Computer Vision 81 (02 2009). https://doi.org/10.1007/s11263-008-0152-6

Digital Library

[11]

Steve Macenski and Ivona Jambrecic. 2021. SLAM Toolbox: SLAM for the dynamic world. Journal of Open Source Software 6, 61 (2021), 2783. https://doi.org/10.21105/joss.02783

[12]

Anca Morar, Alin Moldoveanu, Irina Mocanu, Florica Moldoveanu, Ion Emilian Radoi, Victor Asavei, Alexandru Gradinaru, and Alex Butean. 2020. A Comprehensive Survey of Indoor Localization Methods Based on Computer Vision. Sensors 20, 9 (2020). https://doi.org/10.3390/s20092641

[13]

Christina Ramer, Julian Sessner, Michael Scholz, Xu Zhang, and Jörg Franke. 2015. Fusing low-cost sensor data for localization and mapping of automated guided vehicle fleets in indoor applications. In 2015 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI). 65–70. https://doi.org/10.1109/MFI.2015.7295747

[14]

Nayabrasul Shaik, Matthias Lutz, and Christian Schlegel. 2020. 2D Localization in Large Areas Using Inexpensive RGBD Camera Augmented With Visual Tags. 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) 1 (2020), 613–619.

[15]

Jae Shim and Young Cho. 2016. A Mobile Robot Localization via Indoor Fixed Remote Surveillance Cameras †. Sensors 16 (02 2016), 195. https://doi.org/10.3390/s16020195

[16]

Retnam Visvanathan, Syed Muhammad Mamduh, Kamarulzaman Kamarudin, Ahmad Shakaff Ali Yeon, Ammar Zakaria, Ali Yeon Md Shakaff, Latifah Munirah Kamarudin, and Fathinul Syahir Ahmad Saad. 2015. Mobile robot localization system using multiple ceiling mounted cameras. 2015 IEEE SENSORS (2015), 1–4.

[17]

Stefan Zickler, Tim Laue, Oliver Birbach, Mahisorn Wongphati, and Manuela Veloso. 2010. SSL-Vision: The Shared Vision System for the RoboCup Small Size League. In RoboCup 2009: Robot Soccer World Cup XIII, Jacky Baltes, Michail G. Lagoudakis, Tadashi Naruse, and Saeed Shiry Ghidary (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 425–436.

Cited By

Raghavan DKrishnapuram RAmrutur B(2024)Zero-Shot Pose Estimation and Tracking of Autonomous Mobile Robots using Infrastructure Vision Sensors - An End-to-End Perception FrameworkProceedings of the Fifteenth Indian Conference on Computer Vision Graphics and Image Processing10.1145/3702250.3702269(1-9)Online publication date: 13-Dec-2024
https://dl.acm.org/doi/10.1145/3702250.3702269

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cover image ACM Other conferences

AIR '23: Proceedings of the 2023 6th International Conference on Advances in Robotics

July 2023

583 pages

ISBN:9781450399807

DOI:10.1145/3610419

Copyright © 2023 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 02 November 2023

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AIR 2023

AIR 2023: Advances In Robotics - 6th International Conference of The Robotics Society

July 5 - 8, 2023

Ropar, India

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Cited By

Raghavan DKrishnapuram RAmrutur B(2024)Zero-Shot Pose Estimation and Tracking of Autonomous Mobile Robots using Infrastructure Vision Sensors - An End-to-End Perception FrameworkProceedings of the Fifteenth Indian Conference on Computer Vision Graphics and Image Processing10.1145/3702250.3702269(1-9)Online publication date: 13-Dec-2024
https://dl.acm.org/doi/10.1145/3702250.3702269

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