Chapter 3: Perception and Sensor Fusion
6 sections
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Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- 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.
Key Concepts
- -- Multimodal Sensing
- The integration of data from various sensor modalities to gain a comprehensive understanding of the environment.
- -- SLAM
- Simultaneous Localization and Mapping, a technique that allows a robot to map an unknown environment while keeping track of its location within that map.
- -- Sensor Calibration
- The process of adjusting and correcting sensor readings for systematic errors and aligning multiple sensors for accurate data fusion.
- -- Noise Modeling
- The practice of quantifying and managing the randomness in sensor data to enhance the accuracy of measurements.
- -- Kalman Filter
- An algorithm that estimates a system's state over time by combining predictions and noisy measurements.
- -- RealTime Data Processing
- The capability of processing sensory data instantaneously to facilitate immediate responses in robotic systems.
Additional Learning Materials
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