GPS (Global Positioning System) is widely used for localization in autonomous vehicles due to its ability to provide accurate position and time information.

Mapping in autonomous vehicles helps create a representation of the environment, enabling the vehicle to navigate and make decisions.

FastSLAM (Fast Simultaneous Localization and Mapping) is a widely used algorithm for SLAM due to its efficiency and accuracy.

Signal interference from tall buildings and other structures can degrade GPS accuracy, making localization challenging in urban environments.

Lidar (Light Detection and Ranging) sensors measure distances to objects, providing valuable information for localization and obstacle detection.

Kalman filter is a widely used sensor fusion technique that combines data from multiple sensors to provide a more accurate and reliable estimate of the vehicle's state.

Odometry involves measuring wheel rotations to estimate the vehicle's position and orientation, contributing to localization.

Radar (Radio Detection and Ranging) sensors measure distances to objects, providing valuable information for localization and obstacle detection.

Occupancy grid mapping represents the environment as a grid of cells, where each cell indicates the probability of occupancy by an object.

Cameras can detect traffic signs, providing valuable information for localization and navigation.

Moving objects, such as pedestrians and vehicles, pose a challenge for mapping in dynamic environments, requiring real-time updates.

Topological mapping represents the environment as a graph of interconnected nodes and edges, capturing the connectivity and relationships between different locations.

IMU sensors measure vehicle orientation, providing valuable information for localization and navigation.

Loop closure in SLAM involves detecting loops in the vehicle's trajectory and using them to correct localization errors and improve map accuracy.