MEMS LiDAR Sensor Simulation for Autonomous Driving: A Novel Framework Using Open-source Tools

Sensor data is essential for the development of methods in autonomous driving like object detection, odometry, or SLAM. MEMS LiDAR sensors can be very valuable for autonomous vehicles because they are less prone to shock and wear compared to motorized optomechanical LiDAR sensors. Recording real-world data is complicated and expensive. An alternative is simulated data, but for MEMS LiDAR sensors there is no publicly available software to simulate this type of sensor. With this paper, we introduce a method to simulate data recorded by a MEMS LiDAR sensor like the Blickfeld Cube 1 (and other MEMS LiDAR sensors as well). For this, we use the opensource autonomous driving simulation environment CARLA. Our method can be used to generate a large amount of MEMS LiDAR data faster and cheaper than it would be possible using realworld sensors). We compare our synthetic point cloud with a real-world point cloud and evaluate the similarity. Moreover, we demonstrate the application of our method on the problem of the optimal sensor configuration.