Habitat Point Goal Navigation

Embodied Visual Navigation in Habitat

The aim of this work is to solve the embodied point goal navigation task in photo-realistic, indoor environments using Habitat. In this task, a virtual agent (robot) starts at a random position in an unknown environment. The agent is given the coordinates of a goal location. Primary aim of the agent is to navigate to the goal while taking the most optimal path. This is not a trivial task in

Environment perception stack for Self Driving Cars

This projects implements detailed environment perception stack for self driving cars. A semantic segmentation output of an image computed using Convolutional Neural Network is used as an input to the Environment perception stack. This stack constitutes 3 important sub-stacks as follows:   Estimating the ground plane using RANSAC:To estimate the drivable surface for a car, pixels corresponding to the ground plane in the scene were computed. This extends to finding