  This paper solves the classical problem of simultaneous localization and
mapping (SLAM) in a fashion which avoids linearized approximations altogether.
Based on creating virtual synthetic measurements, the algorithm uses a linear
time- varying (LTV) Kalman observer, bypassing errors and approximations
brought by the linearization process in traditional extended Kalman filtering
(EKF) SLAM. Convergence rates of the algorithm are established using
contraction analysis. Different combinations of sensor information can be
exploited, such as bearing measurements, range measurements, optical flow, or
time-to-contact. As illustrated in simulations, the proposed algorithm can
solve SLAM problems in both 2D and 3D scenarios with guaranteed convergence
rates in a full nonlinear context.

Robotics