What is SLAM in robotics and Self-driving cars

The need for high definition maps, mapping of unknown GPS-denied areas, exploration and autonomous mission are the main motivation for Solving the Simultaneous Localization And Mapping Problem “SLAM” Given a map, a robot could localize itself easily by comparing its observations to the stored map. Given a robot pose relative to a global axis “localization”

What is the difference between SLAM and visual odometry?

First, we have to distinguish between SLAM and odometry. Odometry is a part of SLAM problem. It estimates the agent/robot trajectory incrementally, step after step, measurement after measurement. new_state=old_state+step_measurement The next state is the current state plus the incremental change in motion. Incremental change can be measured using various sensors. Wheel encoders for instance, counts