Multi Sensor Dataset

Multi-sensor datasets are collections of synchronized data from diverse sensors (e.g., cameras, LiDAR, radar) used to improve the robustness and accuracy of perception and mapping systems in various applications, including autonomous driving and robotics. Current research focuses on developing methods for effectively fusing data from these disparate sources, often employing deep learning models to achieve sensor invariance and improve generalization across different sensor configurations and environments. These datasets are crucial for advancing the development of algorithms for tasks such as 3D reconstruction, simultaneous localization and mapping (SLAM), and object detection, ultimately leading to more reliable and capable autonomous systems.