Browsing by Subject "navigation"

Combining data from visual and inertial sensors effectively reduces inherent errors in each modality, enhancing the robustness of sensor-fusion for accurate 6-DoF motion estimation over extended periods. While traditional SfM and SLAM frameworks are well established in literature and real-world applications, purely end-to-end learnable SfM and SLAM networks are still scarce. The adaptability of fully trained models in system configuration and navigation setup holds great potential for future developments in this field. This thesis introduces and assesses two novel end-to-end trainable sensor-fusion models using a supervised learning approach, tested on established navigation benchmarks and custom datasets. The first model utilizes optical flow, revealing its limitations in handling complex camera movements present in pedestrian motion. The second model addresses these shortcomings by using feature point-matching and a completely original design.