Odometry and Sensor Fusion

Related lectures (32)

Page 1 of 4

Explores robot localization through positioning systems, odometry, sensor fusion, and error modeling.

Covers the theory and application of Kalman Filters for estimation and localization in robotics.

Explores Kalman filters, flocking behaviors, and decentralized control in distributed intelligent systems.

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Explores environmental noise, its characteristics, impact on health, measurement methods, and legal regulations.

Explores the fundamentals of mobile robotics, emphasizing uncertainties in localization, sensor fusion, and the Extended Kalman Filter.

Explores environmental legislation, noise protection, and exposure limits for road traffic and aircraft noise.

Explores noise in electronics, covering average power, signal-to-noise ratio, deterministic and random signals, and noise amplification.

Explores image processing techniques such as contrast manipulation, enhancement, and sharpening using Fiji software.

Introduces the rigorous approach to sensor fusion for modern applications.