Simultaneous Localisation and Mapping with Fiducial Markers Based on Extended Kalman Filter

Localisation ability is a prerequisite for the realisation of autonomous systems. In this paper, the extended Kalman filter is used to fuse the encoder information with the information of the camera fiducial markers detection and realise the SLAM algorithm. A new observation model is used considering both the position and orientation of the fiducial markers. The algorithm is implemented using robot operating system and tested in simulation and real environments. The test results show that the algorithm can achieve the expected result, and prove that multi-information fusion has a better effect than single-sensor positioning.