The role of Bayesian bounds in comparing SLAM algorithms performance

Authors:
Andrea Censi
Affiliations:
California Institute of Technology
Venue:
PerMIS '08 Proceedings of the 8th Workshop on Performance Metrics for Intelligent Systems
Year:
2008

Citing 5
Cited 0

Using Occupancy Grids for Mobile Robot Perception and Navigation

Computer
Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking

Bayesian Bounds for Parameter Estimation and Nonlinear Filtering/Tracking
A survey of convergence results on particle filtering methods forpractitioners

IEEE Transactions on Signal Processing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters

IEEE Transactions on Robotics
Convergence and Consistency Analysis for Extended Kalman Filter Based SLAM

IEEE Transactions on Robotics

Quantified Score

Hi-index	0.00

Visualization

Abstract

It is certainly hard to establish performance metrics for intelligent systems. Thankfully, no intelligence is needed to solve SLAM at all. Actually, when we cast the SLAM problem in the Bayesian framework, we already have a formula for the solution --- SLAM research is essentially about finding good approximations to this computationally monstrous formula. Still, SLAM algorithms are difficult to analyze formally, partly because of such out-of-model ad hoc approximations. This paper explains the role of Bayesian bounds in the analysis of such algorithms, according to the principle that sometimes it is better to analyze the problem than the solutions. The theme is explored with particular regard to the problem of comparing algorithms using different representations and different prior information.