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9 Types of Sensor Fusion Algorithms

thinkautonomous.ai · 1,438 words · saved by 1 readers

To understand better, let's consider a simple example of a LiDAR and a Camera both looking at a pedestrian 🚶🏻. Since sensors are noisy, sensor fusion algorithms have been created to consider that noise, and make the most precise estimate possible. The most common types of fusion is by abstraction level. In this case, we're asking the question " When should we do the fusion?" In my article on LiDAR and Camera Fusion , I describe two processes called EARLY and LATE fusion. In the industry, people have other names for this:Low Level, Mid-Level, and High-Level Sensor fusion. ✅ This type of fusion has a lot of potential for the future years, since it considers all the data. ❌ Early Fusion (Low-Level) was very hard to do until a few years ago, because the processing required is huge. At each millisecond, we can fuse hundreds of thousands of points with hundreds of thousands of pixels. Here's an example of Low-Level Fusion for a camera and a LiDAR. Object detection is used in the process, b

In autonomous vehicles, Sensor Fusion is the process of fusing data coming from multiple sensors. The step is mandatory in robotics as it provides more reliability , redundancy , and ultimately, safety . To understand better, let's consider a simple example of a LiDAR and a Camera both looking at a pedestrian 🚶🏻. If one of the two sensors doesn't see the pedestrian, we'll use the other as a crutch to increase our chances of detecting it. We're doing REDUNDANCY . If both are detecting the pedestrian, Sensor Fusion will give us a a more accurate and confident understanding of the pedestrian's

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