ViNU: Vision-Navigation Dataset for Unstructured Environment

The dataset was initially gathered using the Robot Pomona, equipped with an RGBD camera, lidar, IMU, and GPS. This diverse sensor setup allowed for the collection of color images, depth images, GNSS data, point clouds, and IMU data. Initially, all data were captured and stored in a ROS bag format. Subsequent to the initial collection, the data were synchronized offline and transferred to a new ROS bag to ensure accurate alignment across the different data types. Finally, the synchronized data were organized and extracted into separate folders for each data type, resulting in distinct datasets for color images, depth images, GNSS, point clouds, and IMU data. This structured organization facilitates easier access and use for research and development in navigating unstructured environments. Potential Applications: Autonomous Navigation: Enhancing the ability of autonomous vehicles to navigate in complex, unstructured environments such as off-road terrains or disaster-stricken areas. Robotics Research: Providing a rich source of sensor data for developing and testing algorithms related to simultaneous localization and mapping (SLAM), object recognition, and path planning. Augmented Reality: Supporting AR applications that require real-time environmental mapping and interaction. Environmental Monitoring: Assisting in tasks that involve monitoring changes in environments over time, useful in ecological research or urban development planning. Machine Learning: Serving as a training and validation dataset for machine learning models focused on sensor fusion, depth perception, and predictive analytics in dynamic scenarios.