Example Concept: SLAM algorithms incorporate probabilistic frameworks to address uncertainty in sensor data by maintaining a belief distribution over possible robot poses and map configurations. Techniques like the Extended Kalman Filter (EKF) or Particle Filters are used to predict and update these distributions, allowing the system to refine estimates of both the robot's location and the map as new sensor information becomes available. This approach helps in accommodating noise and errors inherent in sensor measurements, leading to more robust and accurate mapping and localization.