Developed a full-autonomy software stack for a F1/10 scaled autonomous racecar in ROS framework. Inclued autonomy modules- time-to-collision based automatic emergency braking, follow-the-gap reactive method, point-to-line metric based iterative-closest-point algorithm for scan-matching, A* and RRT* path-planner, April-tag based pose prediction, raceline optimization, trajectory tracking using Pure Pursuit and Model Predictive Control.
A novel deep learning architecture capable of generating realistic occluded compositions by placing provided individual objects in the scene, in a context-aware manner while preserving the original full masks of the occluded object. Used a combined STN-GAN framework to learn a projection matrix based on encoded geometry and semantic information. Evaluated the generated compositions by fine-tuning a pre-trained Mask-RCNN for the task of amodal instance segmentation and reported the COCO-style mean-Average Precision (mAP) metric.
A Particle Filter based approach for real-time object tracking using mobile robots with an RGB-D camera. The particle filter estimates the location of the object in the global frame and updates the weight of the particles by computing correlation using the 2D feature descriptors of the object inside the bounding box detection. The bounding box detection was obtained using the MobileNet-SSD detection framework.
Implemented transfer learning on ResNet for image classification, and generative architectures:- Variational AutoEncoder, Least-Square GAN and DCGAN for image generation.
Optical flow model to track multi-object moment in videos. Implemented Lucas-Kanade feature tracker and pyramidal KLT tracker to estimate object moments in successive video frames.
Created a Python API to stitch multiple images with overlapping field of views to create a panaromic effect employing Harris Corner Detector, Adaptive Non-Maximal Suppression, Histogram of Oriented Gradients feature descriptors, outlier rejection and homography estimation using RANSAC algorithm.