Lab Meeting December 23rd, 2009 (Shao-Chen): Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case

Title: Multi-robot SLAM with Unknown Initial Correspondence: The Robot Rendezvous Case (IROS 2006)

Authors: Xun S. Zhou and Stergios I. Roumeliotis

Abstract:

This paper presents a new approach to the multi-

robot  map-alignment  problem  that  enables  teams  of  robots  to

build joint maps without initial knowledge of their relative poses.

The  key  contribution  of  this  work  is  an  optimal  algorithm  for

merging  (not  necessarily  overlapping)  maps  that  are  created

by  different  robots  independently.  Relative  pose  measurements

between pairs of robots are processed to compute the coordinate

transformation  between  any  two  maps.  Noise  in  the  robot-

to-robot  observations,  propagated  through  the  map-alignment

process,  increases  the  error  in  the  position  estimates  of  the

transformed  landmarks,  and  reduces  the  overall  accuracy  of

the merged map. When there is overlap between the two maps,

landmarks that appear twice provide additional information, in

the form of constraints, which increases the alignment accuracy.

Landmark  duplicates  are  identified  through  a  fast  nearest-

neighbor  matching  algorithm.  In  order  to  reduce  the  compu-

tational  complexity  of  this  search  process,  a  kd-tree  is  used

to  represent  the  landmarks  in  the  original  map.  The  criterion

employed for matching any two landmarks is the Mahalanobis

distance.  As  a  means  of  validation,  we  present  experimental

results obtained from two robots mapping an area of 4,800 m

2.

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