Chunxin Qiu

“We approve of this direction, not just following in others’ steps. Therefore, whether it’s a bubble or a cake, we will throw ourselves into it as always”, Chunxin Qiu commented on LiDAR about autonomous driving. In 2014, Qiu founded RoboSense, a company focused on developing high end LiDAR applied to autonomous driving.

During his PhD, Qiu took a keen interest in LiDAR. In 2007, Qiu’s PhD research subject was “Environment Perception Technology of Outdoor Mobile Robots," which studied outdoor automobile robots, i.e. autonomous vehicles. Amidst the research, Qiu was much inspired by the work of extracting the three-dimensional features of environments. 

In early 2014, Qiu built the first demo for his LiDAR. In only half a year, Qiu started RoboSense. He said, “Doing business has become an instinct for me. A real instinct. We know it’s hard. But we Chaozhou people are renowned for our diligence and business sense. Since the first day we established our company, we have been running it in a commercialized manner. Only with good products can we make our passion concrete.” 

No matter how excellent the algorithm is, data can’t be extracted. If they want to make a technical breakthrough to acquire better environment perception capabilities, they need to master a much more underlying technology. Qiu then set his mind to develop his own sensor. 

In 2018, RoboSense launched its latest MEMS LiDAR M1 Pre with a detection range of over 200 meters, horizontal angles and vertical angles of 63°*20°, refresh frame rate of 20 fps, and angular resolution of 0.09° *0.2 °. When detecting a person with a height of 1.70 meters at a distance of 50 meters, a traditional 16-beam LiDAR detects one line, a 64-beam LiDAR detects 5 lines and M1 Pre can detect 10 lines.

“MEMS LiDAR has 4 advantages of being low cost, easy for mass production, automotive grade, and high resolution, which can basically solve all the trouble spots for mechanical LiDAR,”Qiu said, “accelerate the penetration of LiDAR into all areas in autonomous driving projects, and speed up the implementation of autonomous driving projects.”