This study investigates the integration of monocular CAD and RGB-based 6D pose estimation into robotic lawnmowers, focusing on charging station docking. A systematic literature review establishes the foundation by exploring methodologies, models, architectures, datasets, and relevant topics from existing literature on 6D pose estimation with CAD and RGB data. An approach for system development was developed integrating YOLOv7 and MegaPose, both deep learning-based, as the models for object detection and 6D pose prediction respectively. The detector model’s and 6D pose predictor’s performance across diverse datasets and distances highlights its potential, though specific challenges suggest the need for further refinement. A methodology for implementing an intelligent 6D pose estimation system for robotic lawnmower operation is developed through the Iterative Development Process where the results demonstrate promising outcomes. Notably, the detector model, trained exclusively on synthetically generated data, exhibits robust performance in detecting charging stations. The subsequent 6D pose predictor, MegaPose, demonstrates robust performance during neutral robotic lawnmower operation and scenarios with artificial degradation such as occlusion and water on the camera lens. Challenges encountered in real-world scenarios, such as low camera height, occlusions at close distances, and unclear imagery underscore the need for future improvements. This study bridges theoretical concepts with real-world implementation, laying a foundation for further advancements. The findings provide valuable insights for the robotic lawnmower domain and potentially for other robotic systems and domains by introducing them to or improving their capabilities via advanced autonomous navigation and interaction.