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Perception forms the basis for intelligent robotic behavior, enabling robots to interpret and engage with their environments through data from various sensors. The chapter delves into multimodal sensing, 3D environmental understanding, and the integration of sensory data using probabilistic models to facilitate autonomous navigation and decision-making in dynamic settings.
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Term: Multimodal Sensing
Definition: The integration of data from various sensor modalities to gain a comprehensive understanding of the environment.
Term: SLAM
Definition: Simultaneous Localization and Mapping, a technique that allows a robot to map an unknown environment while keeping track of its location within that map.
Term: Sensor Calibration
Definition: The process of adjusting and correcting sensor readings for systematic errors and aligning multiple sensors for accurate data fusion.
Term: Noise Modeling
Definition: The practice of quantifying and managing the randomness in sensor data to enhance the accuracy of measurements.
Term: Kalman Filter
Definition: An algorithm that estimates a system's state over time by combining predictions and noisy measurements.
Term: RealTime Data Processing
Definition: The capability of processing sensory data instantaneously to facilitate immediate responses in robotic systems.