AB - 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. ©2010 IEEE.
AU - Huang, S
AU - Lai, Y
AU - Frese, U
AU - Dissanayake, G
DA - 2010/12/01
DO - 10.1109/IROS.2010.5652603
EP - 3016
JO - IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings
PY - 2010/12/01
SP - 3011
TI - How far is SLAM from a linear least squares problem?
Y1 - 2010/12/01
Y2 - 2023/06/04
ER -