Optimization of the simultaneous localization and map-building algorithm for real-time implementation

Abstract

Abstract. This work addresses real time implementation of the Simultaneous Localization and Map Building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filter that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost O(N a 2 ), where N a is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.

Power-SLAM: A linear-complexity, consistent algorithm for SLAM

“…Guivant and Nebot's [10] Compressed EKF (CKF) approach combines the ideas of sub-maps and relative maps. By using sub-maps, this algorithm has complexity O(N a 2 ), where N a is the number of features in the local map.…”

Robocentric map joining: Improving the consistency of EKF-SLAM

“…Many recent efforts have concentrated on reducing the computational complexity of SLAM in large environments [16,13,10,17]. However, only recently, the consistency issues of the EKF-SLAM algorithm have attracted the attention of the research community.…”

“…It is assumed that recursive propagation of the mean and the covariance of those pdfs conveniently approximates the optimal solution of this estimation problem. Many successful implementations of this approach have been reported in indoor [11], outdoor [13], underwater [15] and airborne [14] applications.…”

Visually-Aided Localisation for Autonomous Agricultural Vehicles

“…A variety of probabilistic frameworks have been used for solving the SLAM problem including the Kalman filter and its variants (Guivant & Nebot 2001;Davison & Murray 2002), particle filter (Montemerlo et al 2002;Carlone et al 2010) as well as graph based optimisation methods (Kaess et al 2011;Thrun & Montemerlo 2006) In this thesis we are most interested in localising in large outdoor environments.…”

“…An early example of using SLAM to perform localisation in an outdoor environment is Guivant & Nebot (2001) who used a scanning laser and wheel encoders to perform SLAM using an EKF framework in a park setting, using trees in the park as landmarks.…”