Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment
This addresses the scaling issue in monocular SLAM for robotics and computer vision applications, though it appears incremental as it builds on existing LSD-SLAM methods.
The paper tackles the problem of converting LSD-SLAM point clouds from monocular SLAM into real-world coordinates with metric scale using IMU data, presenting a closed-form solution for transformation and achieving accurate 3D environment reconstruction.
SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable informa- tion about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space trans- formation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.