Towards Global Localization using Multi-Modal Object-Instance Re-Identification

Aneesh Chavan*1, Vaibhav Agrawal*1, Vineeth Bhat†1,
Sarthak Chittawar†1, Siddharth Srivastava3, Chetan Arora2, K Madhava Krishna1
1Robotics Research Centre, IIIT Hyderabad 2IIT Delhi 3Typeface Inc.
*Indicates Equal Contribution
†Indicates Equal Contribution
Paper Datasets Code arXiv
Teaser Image

Overview of our localization pipeline. Our localization framework maps objects from posed RGB-D images and matches query objects using the ReID module (DATOR) to estimate pose.

Abstract

Re-identification (ReID) is a critical challenge in computer vision, predominantly studied in the context of pedestrians and vehicles. However, robust object-instance ReID, which has significant implications for tasks such as autonomous exploration, long-term perception, and scene understanding, remains underexplored.

In this work, we address this gap by proposing a novel dual-path object-instance re-identification transformer architecture that integrates multimodal RGB and depth information. By leveraging depth data, we demonstrate improvements in ReID across scenes that are cluttered or have varying illumination conditions.

Additionally, we develop a ReID-based localization framework that enables accurate camera localization and pose identification across different viewpoints. We validate our methods using two custom-built RGB-D datasets, as well as multiple sequences from the open-source TUM RGB-D datasets.

Our approach demonstrates significant improvements in both object instance ReID (mAP of 75.18) and localization accuracy (success rate of 83% on TUM-RGBD), highlighting the essential role of object ReID in advancing robotic perception. Our models, frameworks, and datasets have been made publicly available.

Video Presentation

BibTeX

@misc{chavan2024globallocalizationusingmultimodal,
      title={Towards Global Localization using Multi-Modal Object-Instance Re-Identification}, 
      author={Aneesh Chavan and Vaibhav Agrawal and Vineeth Bhat and Sarthak Chittawar and Siddharth Srivastava and Chetan Arora and K Madhava Krishna},
      year={2024},
      eprint={2409.12002},
      archivePrefix={arXiv},
      primaryClass={cs.RO},
      url={https://arxiv.org/abs/2409.12002}, 
}