Object-Based Change Detection Algorithm with a Spatial AI Stereo Camera

Area of Science:

• Computer Vision
• Robotics
• Artificial Intelligence

Background:

• Real-time 3D change detection is crucial for autonomous systems.
• Existing methods often lack object-level semantic understanding.
• Integrating advanced sensors like stereo cameras with AI is key for spatial awareness.

Purpose of the Study:

• To develop a real-time, object-based 3D change detection method.
• To create an algorithm capable of maintaining metric-semantic maps for environmental monitoring.
• To leverage the ZED 2 stereo camera's capabilities for enhanced spatial AI.

Main Methods:

• Developed an object-based 3D change detection algorithm using semantic object maps.
• Utilized the ZED 2 stereo camera, integrating stereo vision and AI.
• Conducted extensive camera evaluations for depth, tracking, and object detection accuracy.

Main Results:

• The proposed method achieves real-time object-level 3D change detection.
• The algorithm successfully maintains object-oriented metric-semantic maps.
• Demonstrated the system's effectiveness in diverse real-world indoor and outdoor scenarios.

Conclusions:

• The object-based 3D change detection method is effective and suitable for spatial AI.
• The ZED 2 camera provides reliable data for developing such AI applications.
• This approach advances environmental monitoring and autonomous navigation capabilities.