Samiran Gode

Hello! I am a recent graduate of Carnegie Mellon University where I was fortunate to be a part of the Robot Perception Lab. At CMU, I worked on Underwater Perception and Object SLAM, I now work for a startup as a Robotics Software Engineer.

Before this I interned with Jupiter's Data Science team, focusing on NLP for FAQ search and user behavior analytics. Additionally, I contributed to the EqWATER project at IISc, specializing in leak detection for smart water distribution systems. I've also gained valuable experience as an Area Manager Intern at Amazon.

Email / CV / Google Scholar / Github / Linkedin

News

[Jan'24] SONIC accepted at ICRA 2024.
[Oct'23] Participated in Closing the Loop on Localization workshop at IROS 2023.
[Oct'23] Presented our work SONIC at the Advanced Marine Robotics Workshop at IROS 2023.
[Sep'23] Submitted one paper to ICRA'24
[Aug'23] Our paper was published at the AI Magazine!
[Feb'23] Presented our work on Understanding Political Polarization using Language Models at AAAI'23 AI4CEW
[Dec'22] Graduated from CMU!

Research

	SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars Samiran Gode, Akshay Hinduja, Michael Kaess, ICRA, 2024 2nd Advanced Marine Robotics TC Workshop IROS, 2023 arXiv - Solved data association for underwater SLAM through a novel method for sonar image correspondence using Learned Features. - Introduced a pose-supervised network that generates feature descriptors robust to changes in viewpoints, enabling more reliable feature matches in sonar based localization and mapping.
	Understanding Political Polarization using Language Models: A dataset and method Samiran Gode, Supreeth Bare, Bhiksha Raj, Hyungon(Clay) Yoo AI Magazine The AAAI 2023 Second Workshop on AI for Credible Elections Journal / arXiv / Code - Finetuned Longformer on a scraped Wikipedia dataset to find most important tokens based on attention score. - Used other conventional techniques such as Word2Vec, Doc2Vec and BERT based models understand words which lead to polarisation. - Paper accepcted at AAAI 2023 Workshop on AI for credible elections and selected for publication at the AI Magazine Fall 2023.
	Detecting and Localizing Leaks in Intermittent Water Distribution Networks Samiran Gode, Sheetal Kumar K R, Sindhu H J, P G Prasad, M S Mohan Kumar, Rajesh Sundaresan, - Developed an algorithm for detecting and localizing multiple leaks in Water Distribution Systems with Intermittent water supply for Bengaluru a city of 8.5mil.(As part of EqWATER funded by Ministry of Human Resources Development, Govern- ment of India). - Automated intermittent water supply in an experimental system(60m long*100mm dia network, 4L/s) using LabVIEW to generate scaled-down comparable data with identical disturbances and leaks analogous to field data.
	3D Dense SLAM system using ICP - Camera Localisation on the ICL-NUIM dataset using Iterative Closest Point Algorithm. - Used point-based fusion to create a point cloud map
	NERF (Volume Rendering and Neural Radiance Fields) Code - Implemented a Differentiable Renderer for emission-absorption volumes. - Implemented a ray sampler for optimising volume parameters. - Used a MLP to map 3D positions to Volume Density and colour
	Quadric SLAM - Implemented on Object based semantic SLAM, created low-memory metric semantic maps for multi-robot communication. - Formulated a graph based SLAM. Used quadric factors with the factor graph with underlying visual inertial odometry. - Designed feature descriptors for SONAR using unsupervised learning for underwater SLAM. - Used an encoder decoder structure with CNNs with custom loss functions to learn without labels.
	Particle Filter - Monte Carlo Localization(MCL) based robot localization for an indoor robot using laser rangefinder and odometry. - Implemented the raycasting based sensor and motion model along with resampling.
	Single View to 3D Learning 3D representations using single views.

Thank you to Jon Barron for the website template!