publications

2023

1. RMap: Millimeter-Wave Radar Mapping Through Volumetric Upsampling

   Ajay Narasimha Mopidevi, Kyle Harlow, and Christoffer Heckman

   arXiv preprint arXiv:2310.13188, 2023

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   Millimeter Wave Radar is being adopted as a viable alternative to lidar and radar in adverse visually degraded conditions, such as the presence of fog and dust. However, this sensor modality suffers from severe sparsity and noise under nominal conditions, which makes it difficult to use in precise applications such as mapping. This work presents a novel solution to generate accurate 3D maps from sparse radar point clouds. RMap uses a custom generative transformer architecture, UpPoinTr, which upsamples, denoises, and fills the incomplete radar maps to resemble lidar maps. We test this method on the ColoRadar dataset to demonstrate its efficacy.

   @article{mopidevi2023rmap,
     title = {RMap: Millimeter-Wave Radar Mapping Through Volumetric Upsampling},
     author = {Mopidevi, Ajay Narasimha and Harlow, Kyle and Heckman, Christoffer},
     journal = {arXiv preprint arXiv:2310.13188},
     year = {2023},
   }

2. Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation

   Harel Biggie, Ajay Narasimha Mopidevi, Dusty Woods, and 1 more author

   arXiv preprint arXiv:2306.09523, 2023

   Bib PDF

   @article{biggie2023tell,
     title = {Tell Me Where to Go: A Composable Framework for Context-Aware Embodied Robot Navigation},
     author = {Biggie, Harel and Mopidevi, Ajay Narasimha and Woods, Dusty and Heckman, Christoffer},
     journal = {arXiv preprint arXiv:2306.09523},
     year = {2023},
   }

3. SemEval

   Quintilian at SemEval-2023 Task 4: Grouped BERT for Multi-Label Classification

   Ajay Narasimha Mopidevi, and Hemanth Chenna

   In Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023), Jul 2023

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   In this paper, we initially discuss about the ValueEval task and the challenges involved in multi-label classification tasks. We tried to approach this task using Natural Language Inference and proposed a Grouped-BERT architecture which leverages commonality between the classes for a multi-label classification tasks.

   @inproceedings{mopidevi-chenna-2023-quintilian,
     title = {Quintilian at {S}em{E}val-2023 Task 4: Grouped {BERT} for Multi-Label Classification},
     author = {Mopidevi, Ajay Narasimha and Chenna, Hemanth},
     booktitle = {Proceedings of the The 17th International Workshop on Semantic Evaluation (SemEval-2023)},
     month = jul,
     year = {2023},
     address = {Toronto, Canada},
     publisher = {Association for Computational Linguistics},
     url = {https://aclanthology.org/2023.semeval-1.222},
     pages = {1609--1612},
   }