Source: Computer Vision Workshop
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Recently, a friend in our knowledge planet "3D Vision from Beginner to Mastery" raised a question, namely: how to get started with the positioning of multi-sensor fusion? This kind of similar problem, most of us will encounter when we are new to a certain field of research, in the face of complex theories and techniques, beginners often feel that they do not know how to start, although there are many relevant knowledge and articles on the Internet, but the good and the bad are uneven, just to find the right information for their own learning is very time-consuming and labor-intensive, and the learning process will inevitably encounter a variety of problems, and the exchange of knowledge between them can not achieve real-time.
On the above question, let's first briefly introduce some positioning solutions for multi-sensor fusion and how to get started!
Autonomous navigation is the core function of robotics and autonomous driving, and with the development of technology, a single sensor can no longer meet the positioning needs in complex environments. Existing robots and autonomous vehicles are often equipped with multiple sensors such as lidar, camera, IMU, GPS, etc. For example, GPS is not as effective indoors or in obscured environments, vision sensors are difficult to work in situations where there is insufficient light or visual signature, and lidar, while providing accurate distance measurements, is expensive and limited in some environments. Therefore, multivariate sensor fusion has become a popular research direction, which combines the advantages of multiple sensors to achieve more accurate and robust positioning.
The research in this field is more about fusing the data of multiple sensors in an appropriate way, so that the positioning algorithm can continuously output robust and accurate estimation results in various challenging environments by complementing each other's advantages.
For the positioning of entry-level multi-sensor fusion, there is a lot of knowledge that needs to be learned, such as understanding the principle of cameras, lasers and other sensors, how to integrate various sensors, optimization after fusion, and so on.
To this end, we have launched a course in "3D Vision from Beginner to Mastery", that is, "Thorough Analysis of Laser-Vision-IMU-GPS Fusion SLAM Algorithm: Theoretical Derivation, Code Explanation and Actual Combat", and the main teacher is also a very powerful boss.
If you are interested, you can join our planet to exchange and learn together.