AI for Fast Object Learning, Detection, and 3D Localization in a Multi-layered Map for an Intuitive User-Interface

Efficient object learning, detection, and 3D localization are critical for autonomous systems op-erating in dynamic environments. This research focuses on developing an AI-driven framework that enables fast object learning with minimal data, robust real-time detection, and precise 3D localization within a multi-layered map representation. The proposed system integrates deep learning-based perception with spatial mapping techniques to create an adaptive environment model that enhances robot autonomy and interaction. A key innovation is using a multi-layered map, which fuses geometric, semantic, and temporal information to provide a comprehensive scene understanding. This allows real-time adaptation to environmental changes and intuitive interaction through a user-friendly interface. The system aims to support applications in mobile robotics, augmented reality, and human-robot collaboration, enabling efficient navigation, ob-ject manipulation, and task planning in complex, unstructured environments. Experimental re-sults demonstrate the approach's effectiveness in achieving high-speed, accurate object learning and localization while maintaining computational efficiency.