In the field of autonomous vehicles, the accurate localization of a vehicle in its environment is crucial for precise movement. However, creating a map of the environment is necessary for the vehicle to locate itself. This presents a challenge, as a map is needed for localization and localization is needed for map creation. Researchers have attempted to solve this problem using methods called Simultaneous Localization and Mapping (SLAM).

Our team at UTBM is participating in the UTAC Challenge for the second year, which requires us to develop a method for localizing a vehicle in an environment with few distinct features, such as the UTAC track, which mainly consists of traffic signals, signs, and trees. Currently, the vehicle's location is determined using a costly GPS RTK sensor, but we aim to propose a method that uses more affordable sensors with comparable accuracy. To achieve this, we installed multiple sensors on the vehicle, including a 3D Lidar (rs-lidar), two cameras, a GPS RTK, and an Inertial Measurement Unit (IMU).

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