Title :
A sparse weight Kalman filter approach to simultaneous localisation and map building
Author :
Julier, Simon J.
Author_Institution :
IDAK Industries, Jefferson City, MO, USA
Abstract :
This paper describes a sparse weight Kalman filter algorithm for simultaneous localisation and map building (SLAM). This algorithm trades optimality for a form of the weight equation which confers computational advantages. For a map of n beacons, the storage is O(n2) and the computational costs are O(n). We show that, in a simulation, the method yields results which are similar to the optimal Kalman filter and the suboptimal update method proposed by Guivant et al. (2000)
Keywords :
Kalman filters; computational complexity; mobile robots; optimisation; path planning; SLAM algorithm; computational complexity; localisation; map building; optimisation; path planning; sparse weight Kalman filter; Buildings; Cities and towns; Computational efficiency; Equations; Heuristic algorithms; Kalman filters; Robots; Simultaneous localization and mapping; Vehicles; Yield estimation;
Conference_Titel :
Intelligent Robots and Systems, 2001. Proceedings. 2001 IEEE/RSJ International Conference on
Conference_Location :
Maui, HI
Print_ISBN :
0-7803-6612-3
DOI :
10.1109/IROS.2001.977154