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Divyansh Agarwal
Junior Machine Learning Engineer specializing in computer vision and LLM applications
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About
Built and led an autonomous driving software effort for Formula Student, owning the full autonomy stack (perception, planning, control) orchestrated in ROS. Implemented stereo depth + YOLO object detection, RRT/RRT* planning, and a robust SLAM pipeline (Kalman filter, submapping) while leveraging Gazebo simulation and modern deployment tooling (Docker/Kubernetes, AWS, GitHub Actions CI/CD).
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Key Strengths
- Founded and led an autonomous driving/robotics department for Formula Student
- End-to-end autonomy stack ownership (perception, planning, control) using ROS
- Built stereo-depth + YOLO-based perception pipeline and fused multi-sensor data in ROS
- Implemented RRT/RRT* path planning for autonomous driving
- Optimized SLAM compute load using submapping; integrated Kalman filter for robustness
- Established simulation-to-deployment workflow using Gazebo, Docker/Kubernetes, AWS, and GitHub Actions CI/CD
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Publications
1 publication
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