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Abstract: Robot perception, using sensors and computer vision techniques,  enables robots to understand and interact with their environment, including tasks like navigation, object recognition, and human-robot interaction .   Key Concepts and Techniques: Sensors: Robots use various sensors to gather data about their surroundings, including cameras, LiDARs, radars, IMUs, and tactile sensors.   Computer Vision: Algorithms and techniques are used to process and interpret visual data from sensors, enabling robots to "see" and understand their environment.   Sensor Fusion: Combining data from multiple sensors to create a more accurate and reliable representation of the environment.   Object Detection and Recognition: Identifying and classifying objects in the robot's field of view.   Face Detection and Tracking: Identifying and tracking human faces, useful for human-robot interaction.   Visual Odometry: Determining the robot's position and orien...

Abstract : Sensor data processing and filtering"  refers to the process of taking raw data collected from sensors, cleaning it up, removing unwanted noise or inconsistencies, and transforming it into a usable format for further analysis and interpretation , essentially allowing a system to accurately "perceive" its environment based on the sensor inputs.   Key points about sensor data processing and filtering: Raw data: Sensors produce raw data, which can be affected by noise, errors, and inconsistencies due to environmental factors or sensor limitations.   Cleaning and Preprocessing: The first step is to clean the raw data by removing outliers, correcting for known biases, and applying calibration factors.   Filtering Techniques: Low-pass filters:  Remove high-frequency noise while preserving the overall trend of the data.   High-pass filters:  Remove low-frequency noise while preserving rapid changes in the data.   Bandpass filters: ...