Ghost IV — Sensor Fusion: Encoders + IMU
<p>Why is sensor fusion necessary? It is partly due to the fact that typically no single sensor provides everything that we want to know (or at least not very well). But, it is also due to different sensors having different characteristics, some of which are more desirable than others. A GPS can give an absolute position, but it will have a low update rate, and is subject to discrete jumps. On the other hand, an inertial measurement unit (IMU) can update extremely quickly, but when you try to integrate acceleration over time to obtain position, the errors in doing so grow without bounds over time. What we want is the best of both worlds, hence sensor fusion.</p>
<p>For this project, I’ll be implementing sensor fusion to improve the odometry estimation with encoders from the last story, by combining it with data from an IMU. But first, I’ll take a moment to provide some details on how this actually works. There are a few different approaches to sensor fusion, such as a probabilistic method, or fuzzy logic. I’ll explain how the probabilistic approach works since that is what I am using, and I feel it is the most intuitive.</p>
<p><a href="https://medium.com/hackernoon/ghost-iv-sensor-fusion-encoders-imu-c099dd40a7b"><strong>Website</strong></a></p>