Vision Based Autonomous Navigation for Unmanned Ground Vehicle for Outdoor environment

• 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