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Structure Aided Odometry (SAO) : A Novel Analytical Odometry Technique Based on Semi-Absolute Localization for Precision-Warehouse Robotic Assistance in Environments with Low Feature Variation

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Abstract

In this study, a novel semi-absolute method of localization and subsequent odometry: Structure Aided Odometry (SAO) for precision navigation in low-feature large scale environments, is proposed and validated. Storage racks and ceiling corrugation patterns are used to calculate orientation, cross-track, and along-track estimates for a warehouse robot to produce odometry with minimal drift. A comparison is made with existing laser odometry techniques in both simulated and real (albeit controlled) test environments. The results show a promise of low drift odometry for warehouse robots inside the aisles by offering a drift reduction > 75%. A hybrid odometry technique is also proposed which combines existing odometry with structural cues. It is further validated in simulated environments containing multiple aisles as well as a full warehouse. The results show that augmenting laser with structural features reduces the average root mean square error (RMSE) in both along-track and cross-track directions by at least 70%.

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Availability of data and material

The data used in the paper can be made available at the request of the corresponding author (Zohaib Hasnain).

Code availability

The code used in this paper can be found at https://github.com/karry3775/Elsia_ws

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Acknowledgements

We would like to express our gratitude to Dr. James Hubbard Jr. and Professor Michael Walsh, who were kind enough to provide us access to the motion capture system at RELLIS Starlab Facility. We express sincere thanks to Drew Hubbard and Ameya Deshpande who assisted us in our experiments at the motion capture facility. We would also like to thank Kaustubh Mahesh Tangsali and Madhu Areti for valuable feedback on the paper.

Funding

This project was supported by the funds from Texas A&M Engineering Experiment Station (TEES) startup.

Author information

Authors and Affiliations

  1. Department of Mechanical Engineering, Texas A&M University, College Station, USA

    Kartik Prakash & Zohaib Hasnain

  2. Department of Aerospace Engineering, Texas A&M University, College Station, USA

    Mohamed Naveed Gul Mohamed & Suman Chakravorty

Authors
  1. Kartik Prakash
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  2. Mohamed Naveed Gul Mohamed
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  3. Suman Chakravorty
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  4. Zohaib Hasnain
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Contributions

Kartik Prakash performed the preliminary literature survey and proposed a novel method for structure based localization in warehouse environments. He along with Mohamed Naveed Gul Mohamed carried out the theoretical proofs and experimental validation provided in this paper. Zohaib Hasnain and Suman Chakravorty provided valuable inputs guiding the project from start to finish. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Zohaib Hasnain.

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Kartik Prakash, Mohamed Naveed Gul Mohamed, Suman Chakravorty and Zohaib Hasnain declare that they have no conflict of interest.

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Appendix

Appendix

Fig. 18
figure 18

Root mean squared errors comparison a, b - real and c, d - simulation for Isolated aisle environment

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Fig. 19
figure 19

Terminal error comparison between LPI and SAO where a, b, c, d represents results for real isolated aisle environment

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Fig. 20
figure 20

Terminal error comparison between LPI and SAO where a, b, c, d represents results simulated isolated aisle environment

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Fig. 21
figure 21

Root mean square errors are shown in longitudinal(X) as well as latitudinal(Y) in a and b respectively, for multiple aisle environment

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Fig. 22
figure 22

Root mean square errors are shown in longitudinal(X) as well as latitudinal(Y) in a and b for warehouse environment

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Prakash, K., Mohamed, M.N.G., Chakravorty, S. et al. Structure Aided Odometry (SAO) : A Novel Analytical Odometry Technique Based on Semi-Absolute Localization for Precision-Warehouse Robotic Assistance in Environments with Low Feature Variation. J Intell Robot Syst 102, 72 (2021). https://doi.org/10.1007/s10846-021-01427-w

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  • DOI: https://doi.org/10.1007/s10846-021-01427-w

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