Analytical SLAM without linearization. (December 2017)

• Record Type: Journal Article
• Authors: Tan, Feng; Lohmiller, Winfried; Slotine, Jean-Jacques
• Is Part Of: International journal of robotics research. Volume 36:Number 13/14(2017)
• Page Start: 1554
• Page End: 1578
• Publication Date: 2017-12
• Subjects: Simultaneous localization and mapping -- contraction analysis -- virtual measurement -- linear time-varying Kalman filter; Robots -- Periodicals; Robots, Industrial -- Periodicals; 629.89205
• DOI: 10.1177/0278364917710541
• Languages: English
• ISSNs: 0278-3649

Abstract

This paper solves the classical problem of simultaneous localization and mapping (SLAM) in a fashion that avoids linearized approximations altogether. Based on the creation of virtual synthetic measurements, the algorithm uses a linear time-varying Kalman observer, bypassing errors and approximations brought by the linearization process in traditional extended Kalman filtering 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. SLAM-DUNK, a more advanced version of the algorithm in global coordinates, exploits the conditional independence property of the SLAM problem, decoupling the covariance matrices between different landmarks and reducing computational complexity to O(n). 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.