Vision Based Autonomous Navigation for Unmanned Ground Vehicle for Outdoor environment
“We need to build an automated navigation brain for an outdoor robot using only cameras instead of GPS. This robot must figure out where it is, spot obstacles like rocks and ditches, and safely drive from point A to point B without crashing.”
Deconstructed Problem Blueprint
WHAT TO BUILD?
An autonomous edge-AI software stack combining visual perception, visual SLAM (Simultaneous Localization and Mapping), and local path-planning running on an onboard computer.
WHO IS IT FOR?
Bharat Electronics Limited (BEL), defense forces, and industrial automated ground vehicle operators.
WHY DOES IT EXIST?
GPS signals are frequently jammed, spoofed, or blocked in outdoor, rough, or tactical environments. Relying purely on GPS makes unmanned ground vehicles vulnerable, necessitating robust vision-based navigation.
INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE
Monocular or stereo RGB camera video feeds, optional IMU (Inertial Measurement Unit) data, and target destination coordinates/waypoints.
Deep learning models detect traversable paths and hazards; Visual SLAM computes ego-motion and local position; Costmap generation and local path planners convert data into steering and throttle commands.
Real-time motor control/actuation commands (linear and angular velocity), live visual debugging telemetry, and trajectory mapping on a dashboard.
CONSTRAINTS
- GPS-denied environment execution
- Real-time processing constraint on edge hardware
- Varying outdoor illumination and weather conditions
- Limited compute power on standard edge boards
JURY METRICS
- Inference latency per frame (FPS)
- Trajectory tracking drift error percentage
- Collision-free success rate across varying terrain
- Path planning computation time
CORE BOTTLENECK
Accumulated drift in Visual Odometry over long distances and handling severe lighting transitions (e.g., bright sunlight to heavy shadow).
Why You Should Choose This PS (Pros)
- Direct defense and industrial relevance with high national security value
- Exciting live demonstration potential with simulation or small-scale rover models
- Clear, modular scope: Perception, SLAM, and Planning can be divided cleanly among team members
Challenges & Risks to Watch (Cons)
- High mathematical complexity in Visual SLAM and state estimation
- Risk of hardware failure or calibration issues during live evaluation
- Simulation-to-real-world gap can cause unexpected algorithm failure