Continuous mapping: towards non-rigid SLAM

As technology advances, automation continues to reshape industries, making autonomous systems increasingly dependent on precise mapping for navigation and decision-making. The effectiveness of these systems in complex and dynamic environments relies on ac- curate spatial awareness and reliable localization. Mapping has progressed from basic 2D representations to high-resolution 3D models, offering a deeper understanding of the surroundings. However, conventional Simultaneous Localization and Mapping (SLAM) methods assume rigid transformations, leading to motion distortions that degrade mapping accuracy in real-world applications. Addressing these challenges requires a transition toward non-rigid SLAM and advanced sensor fusion methodologies. This research introduces novel approaches to refine non-rigid SLAM by integrating motion compensation algorithms, multi-sensor fusion, and dynamic trajectory optimiza- tion. Unlike conventional methods, this framework continuously refines pose estimation by correcting measurement distortions, ensuring accuracy and stability over time. By enhanc- ing spatial coherence and reducing trajectory drift, non-rigid SLAM enables autonomous systems to operate more reliably in dynamic, real-world conditions. Recognizing the limitations of existing SLAM datasets, this research also presents a comprehensive robotics perception dataset. It includes LiDAR point clouds, RGB imagery, IMU, and GPS data collected across diverse environments, providing a robust benchmark for assessing motion-aware SLAM strategies. The dataset emphasizes precise calibration and synchronization, utilizing modern equipment to capture various environments. Data collection is conducted both manually and through vehicle-mounted systems, ensuring its applicability to various robotic applications. In summary, non-rigid SLAM represents a significant step toward more adaptable and resilient mapping systems. By addressing sensor motion distortions and refining pose estimation, this research contributes to the evolution of autonomous navigation, where continuous-motion SLAM improves spatial awareness and enhances operational reliability across a diverse range of applications.