Title :
How far is SLAM from a linear least squares problem?
Author :
Huang, Shoudong ; Lai, Yingwu ; Frese, Udo ; Dissanayake, Gamini
Author_Institution :
Fac. of Eng. & Inf. Technol., Univ. of Technol. Sydney Australia, Sydney, NSW, Australia
Abstract :
Most people believe SLAM is a complex nonlinear estimation/optimization problem. However, recent research shows that some simple iterative methods based on linearization can sometimes provide surprisingly good solutions to SLAM without being trapped into a local minimum. This demonstrates that hidden structure exists in the SLAM problem that is yet to be understood. In this paper, we first analyze how far SLAM is from a convex optimization problem. Then we show that by properly choosing the state vector, SLAM problem can be formulated as a nonlinear least squares problem with many quadratic terms in the objective function, thus it is clearer how far SLAM is from a linear least squares problem. Furthermore, we explain that how the map joining approaches reduce the nonlinearity/nonconvexity of the SLAM problem.
Keywords :
SLAM (robots); iterative methods; least squares approximations; nonlinear estimation; nonlinear programming; SLAM problem; complex nonlinear estimation problem; complex nonlinear optimization problem; iterative methods; linear least squares problem; nonlinear least squares problem;
Conference_Titel :
Intelligent Robots and Systems (IROS), 2010 IEEE/RSJ International Conference on
Conference_Location :
Taipei
Print_ISBN :
978-1-4244-6674-0
DOI :
10.1109/IROS.2010.5652603