Simultaneous Localization And Mapping Without Linearization Feng Tan, Winfried Lohmiller and Jean-Jacques Slotine Abstract We apply a combination of linear time varying (LTV) Kalman filtering and nonlinear contraction tools to the problem of simultaneous mapping and local- ization (SLAM), in a fashion which avoids linearized approximations altogether. By exploiting virtual synthetic measurements, the LTV Kalman observer avoids errors and approximations brought by the linearization process in the EKF SLAM. Fur- thermore, conditioned on the robot position, the covariances between landmarks are fully decoupled, making the algorithm easily scalable. Contraction analysis is used to establish stability of the algorithm and quantify its convergence rate. We pro- pose four versions based on different combinations of sensor information, ranging from traditional bearing measurements and radial measurements to optical flows and time-to-contact measurements. As shown in simulations, the proposed algorithm is simple and fast, and it can solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence rates in a full nonlinear context. 1 Introduction Simultaneous localization and mapping (SLAM) is a key problem in mobile robotics research. The Extended Kalman Filtering SLAM (EKF SLAM) approach is the earliest and perhaps the most influential SLAM algorithm. It linearizes a nonlin- ear SLAM model so that Kalman Filter can be used to achieve local approximate estimations. However, this linearization process on the originally nonlinear model introduces accumulating errors which causes the algorithm to be inconsistent and Feng Tan Massachusetts Institute of Technology, e-mail: fengtan@mit.edu Winfried Lohmiller Massachusetts Institute of Technology e-mail: wslohmil@mit.edu Jean-Jacques Slotine Massachusetts Institute of Technology e-mail: jjs@mit.edu 1 arXiv:1512.08829v1 [cs.RO] 30 Dec 2015