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Chapter 36 sections
Multimodal sensors (vision, LiDAR, IMU, tactile) provide robots with diverse environmental data.
SLAM techniques allow autonomous navigation and mapping in unknown environments.
Calibration and noise modeling improve sensor accuracy and reliability.
Bayesian fusion and Kalman filters enable intelligent, probabilistic data integration.
Real-time processing pipelines are critical for responsive robot perception and action.
The integration of data from various sensor modalities to gain a comprehensive understanding of the environment.
Simultaneous Localization and Mapping, a technique that allows a robot to map an unknown environment while keeping track of its location within that map.
The process of adjusting and correcting sensor readings for systematic errors and aligning multiple sensors for accurate data fusion.
The practice of quantifying and managing the randomness in sensor data to enhance the accuracy of measurements.
An algorithm that estimates a system's state over time by combining predictions and noisy measurements.
The capability of processing sensory data instantaneously to facilitate immediate responses in robotic systems.