UGV Sensor Data | 2D & 3D Trajectories

This data was obtained as part of the work of my Master Thesis. The main goal was to acquire data from different sensors in different trajectories (2D and 3D) and fuse them with the help of an Extended Kalman Filter.

The computational environment for data acquisition was ROS, which implements a communication architecture based on nodes and topics. In this structure, each sensor is associated with a specific node, responsible for publishing information through topics.
All data acquisitions are organized using timestamps, a crucial point in data processing and analysis. Unix timestamps are used, representing temporal instances as seconds since January 1, 1970, 00:00:00 UTC. They provide precision on the order of nanoseconds, facilitating temporal calculations and comparisons in computational systems