Learn about the most effective sensor fusion methods for estimating the pose of a robot, such as Kalman filters, factor graphs, deep learning, and hybrid methods.

Summary <p>This chapter provides an in&#x2010;depth look at sensor fusion methods and algorithms, starting with an introduction to the estimation philosophy that guides the design of fusion systems.

Modern fusion algorithms, however, have adaptive weighting techniques which can dynamically adjust sensor input to achieve accuracy when exposed to bad weather conditions. 95% of optimal performance ...

The mathematics that enable sensor fusion include probabilistic modeling and statistical estimation using Bayesian inference and other techniques.

Freescale was an early catalyst of AIC and has added open-source algorithms such as a C source library for 3-, 6- and 9-axis sensor fusion. Sensor fusion is a basic building block for sensor data ...

Sensor fusion is the process of integrating data from multiple sensors to produce more consistent, accurate, and useful information than would be possible from the individual sensors alone. In the ...

An obstacle recognition algorithm for the Pre-Crash Safety system has been newly developed with a stereo vision system and a millimeter wave radar with additional functions. This algorithm uses the ...

Building on the XKF-3™ sensor fusion algorithms for 3D orientation estimation, Xsens is delivering a custom DSP module which outputs very accurate vertical reference values, even in dynamic ...