SLAM stands for Simultaneous Localization And Mapping, a core idea in robotics that lets a robot build a map while also estimating where it is inside that map. This matters because a robot often enters places where no map exists, such as a new home, a warehouse aisle, or a rescue site. The robot must turn sensor measurements into a useful layout of walls, doors, and obstacles.
At the same time, it must keep track of its own changing position so it can move safely and reach goals.
Key Facts
- SLAM = Simultaneous Localization And Mapping.
- Pose describes a robot position and direction: pose = (x, y, θ).
- Distance from speed over time: d = vΔt.
- A lidar point can be estimated by x = r cos θ and y = r sin θ.
- Odometry update for straight motion: x_new = x_old + d cos θ, y_new = y_old + d sin θ.
- Good SLAM combines prediction from motion with correction from sensors.
Vocabulary
- SLAM
- SLAM is the process of building a map of an unknown environment while estimating the robot's own location in that map.
- Localization
- Localization is finding the robot's position and direction relative to a map or starting point.
- Mapping
- Mapping is creating a representation of walls, open space, obstacles, and landmarks from sensor data.
- Lidar
- Lidar is a sensor that measures distances by sending out light pulses and timing their reflections.
- Odometry
- Odometry is estimating motion by measuring wheel rotation, motor movement, or other internal motion data.
Common Mistakes to Avoid
- Treating the first map as perfectly correct is wrong because early sensor readings can be noisy or incomplete, especially near corners and glassy surfaces.
- Ignoring robot orientation is wrong because the same x and y position can face different directions, which changes how sensor beams line up with walls.
- Using odometry alone is wrong because small wheel slip errors build up over time and make the estimated path drift away from the true path.
- Assuming every sensor point is an obstacle is wrong because reflections, moving people, and measurement noise can create false points in the map.
Practice Questions
- 1 A robot drives straight at 0.40 m/s for 5.0 s. How far does odometry predict it moved?
- 2 A lidar measures an obstacle at range r = 4.0 m and angle θ = 60 degrees from the robot's forward direction. Using cos 60 degrees = 0.5 and sin 60 degrees = 0.866, find the obstacle coordinates relative to the robot.
- 3 A robot's wheel odometry says it is 1.0 m farther east than the map and lidar readings suggest. Explain why a SLAM system should not simply trust the odometry estimate.