Paper Info
Title
Hierarchical Linear/Constant Time SLAM Using Particle Filters for Dense Maps
Abstract
We present an improvement to the DP-SLAM algorithm for simultane- ous localization and mapping (SLAM) that maintains multiple hypothe- ses about densely populated maps (one full map per particle in a par- ticle filter) in time that is linear in all significant algorit hm parameters and takes constant (amortized) time per iteration. This means that the asymptotic complexity of the algorithm is no greater than that of a pure localization algorithm using a single map and the same number of parti- cles. We also present a hierarchical extension of DP-SLAM that uses a two level particle filter which models drift in the particle fi ltering process itself. The hierarchical approach enables recovery from the inevitable drift that results from using a finite number of particles in a particle filter and permits the use of DP-SLAM in more challenging domains, while maintaining linear time asymptotic complexity.
Year
Venue
Keywords
2005
NIPS
particle filter,linear time
Field
DocType
Citations 
Particle number,Mathematical optimization,Finite set,Computer science,Particle filter,Monte Carlo localization,Time complexity,Simultaneous localization and mapping,Auxiliary particle filter,Particle
Conference
23
PageRank 
References 
Authors
1.23
11
2
Name
Order
Citations
PageRank
Austin I. Eliazar117611.91
Ronald Parr22428186.85