Vision Based Autonomous Navigation for Unmanned Ground Vehicle for Outdoor environment

Official description

• Background Outdoor Unmanned Ground Vehicles (UGVs) face unpredictable terrain, changing light, and unreliable GPS signals. To achieve true autonomy in applications like search-and-rescue,agriculture, or delivery, UGVs must rely on onboard computer vision. Visual perception provides a cost-effective, data-rich way for vehicles to understand and safely navigate complex,unstructured outdoor surroundings. • Description The objective is to build an autonomous navigation system for a UGV operating in a GPS-denied outdoor environment using camera feeds as the primary sensor. Students must solve three key challenges: 1. Path Detection: Real-time identification of safe, traversable paths vs. hazards (e.g., rocks,ditches, trees). 2. Visual Localization: Estimating the UGV’s position and orientation without GPS using visual data. 3. Collision Avoidance: Dynamically routing the vehicle around sudden obstacles toward a destination. • Expected Solution A functional software module consisting of: • Perception AI: A lightweight model for obstacle and path detection. • Visual SLAM/Odometry: A pipeline to track vehicle movement. • Path Planner: An algorithm to translate visual data into wheel/motor commands. • Success Criteria: Successful, collision-free navigation from Point A to Point B across outdoor scenarios