Yutian Chen

About Me

I am dedicated to improve the spatial understanding ability of autonomous systems, which requires a combination of geometry and semantic information about the surroundings. My research experience on visual-inertial SLAM at the AirLAB focuses on improving the recognition of scene geometry in adversarial conditions, while my research experience in Natural Language Processing (NLP) and semantic segmentation focuses on grasping and interpreting the semantic of scenes.

Recent Research

Below is a highlight list of my recent works. For a full list of works, please see Here.

• UFM: A Simple Path towards Unified Dense Correspondence with Flow
• AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
• MAC-VO: Metric-Aware Covariance for Learning-based Stereo Visual Odometry

  ICRA 2025 Best Conference Paper Award, Best Paper Award on Robot Perception

Projects

PyPose

PyPose is a Library for Robot Learning with Physics-based Optimization. It supports efficient automatic-differentiation on Lie Group and Lie Algebra. I'm an active contributor of the PyPose project.

C0 Visualizer

I designed and implemented the C0 program visualizer in 2022 Summer. It is a virtual machine that executes C0 language (a safe subset of C) and provide visualization and debugging tools for education purpose.

Skills

Robotics

Visual-Inertial SLAM, Computer Vision, Geometric Vision, ROS2, C++

Deep learning & Artificial Intelligence

Natural Language Processing, Semantic Segmentation, PyTorch, CUDA, TensorRT, Python, Triton

Miscellaneous

React, HTML, CSS, TypeScript, LaTeX, Blender

Open Source Notes

I believe knowledge is most impactful when shared freely. By open-sourcing my notes from high school AP courses to advanced university topics, I aim to improve the accessibility of knowledge for everyone.

Experience

1. 

   AI Research Intern

   Field AI Inc.

   Jun 2025-Aug 2025

   Excited to join Field AI Inc. as a Robot Perception Intern, working with Jay Patrikar on accelerating high-resolution visual transformer inference on edge compute devices.

   

   Learning-based Visual-Inertial SLAM

   The AirLab, Robotics Institue, Carnegie Mellon University

   Sep 2022-Now

   Working with Professor Sebastian Scherer, I aimed to construct robust and accurate visual-inertial SLAM system using data-driven approach. I Developed the MAC-VO, a visual odometry that outperforms the state-of-the-art visual odomtries like DPVO by 30% on relative translation error (RTE) and relative rotation error (ROE) in multiple public datasets. I also Deployed the MAC-VO as ROS2 node on Orin-AGX on real drone and speedup the system by 4 times with TensorRT.

   1. UFM: A Simple Path towards Unified Dense Correspondence with Flow
   2. AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability
   3. MAC-VO: Metric-Aware Covariance for Learning-based Stereo Visual Odometry

      ICRA 2025 Best Conference Paper Award, Best Paper Award on Robot Perception

   4. AirIMU: Learning Uncertainty Propagation for Inertial Odometry
   5. PyPose v0.6: The Imperative Programming Interface for Robotics
   

   Embodied AI Simulator

   IBM-MIT Watson AI Lab

   Apr 2024-Jan 2025

   Working with Professor Chuang Gan, I developed a data pipeline for City-scale 3D scene reconstruction based on real world satellite/street-view image for multi-agent simulator.

   1. Work In Progress, Currently under double-blind review
   

   Generated Text Detection

   Language Technology Institue, Carnegie Mellon University

   Mar 2023-Sep 2023

   Working with Professor Rita Singh and Bhiksha Raj, built a LLM-generated content detector called "LLM-Sentinel". Reaches 98% accuracy on test dataset and outperform existing content detector by OpenAI and ZeroGPT. Collected the OpenLLMText dataset, a dataset contains 30k human written text from OpenWebText and its corresponding rephrased version by various LLMs such as GPT3.5, LLaMA, PaLM, etc.

   1. Token Prediction as Implicit Classification to Identify LLM-Generated Text
   

   Medical Image Segmentation

   Guangdong Cardiovascular Institute

   Dec 2018-Jan 2020

   Mentored by Professor Yiyu Shi and Xiaowei Xu, I proposed an encoder-decoder architecture to perform semantic segmentation on cardiac MRI sequence. By introducing Temporal constraint on segmentation result, the model improved the accuracy by 2% on ACDC Dataset comparing to the baseline model.

   1. Myocardial Segmentation of Cardiac MRI Sequences With Temporal Consistency for Coronary Artery Disease Diagnosis

Courses

1. 16-833 Localization and Mapping
2. 16-385 Computer Vision
3. 15-451 Algorithm Design & Analysis
4. 15-418 Parallel Computer Architecture and Programming
5. 11-777 Multi-Modal Machine Learning
6. 10-708 Probablistic Graphical Model
7. 11-785 Intro to Deep Learning