| Volume 21 Issue 10 - Publication Date: 1 October 2002 |
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| Special issue on International
Symposia on Experimental Robotics 2000 |
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| Mapping Partially Observable
Features from Multiple Uncertain Vantage Points |
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| John J. Leonard, Richard
J. Rikoski, Paul M. Newman and Michael Bosse MIT Department
of Ocean Engineering, Cambridge, MA 02139, USA |
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| In this paper we present a
technique for mapping partially observable features from multiple uncertain
vantage points. The problem of concurrent mapping and localization (CML)
is stated as follows. Starting from an initial known position, a mobile
robot travels through a sequence of positions, obtaining a set of sensor
measurements at each position. The goal is to process the sensor data to
produce an estimate of the trajectory of the robot while concurrently building
a map of the environment. In this paper, we describe a generalized framework
for CML that incorporates temporal as well as spatial correlations. The
representation is expanded to incorporate past vehicle positions in the
state vector. Estimates of the correlations between current and previous
vehicle states are explicitly maintained. This enables the consistent initialization
of map features using data from multiple time steps. Updates to the map
and the vehicle trajectory can also be performed in batches of data acquired
from multiple vantage points. The method is illustrated with sonar data
from a testing tank and via experiments with a B21 land mobile robot, demonstrating
the ability to perform CML with sparse and ambiguous data. |
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| Multimedia Key |
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 = Data |
= Code |
= Image |
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Extension |
Type |
Description |
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Example
One: Sonar experiment with two objects. (6.1 MB) |
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Example
Two: Measurement processing for corridor experiment. (8.3 MB) |
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Example
Three: Mapped features for corridor experiment. (0.9 MB) |
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Example
Four: Map and odometry trajectory for large-scale experiment. |
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Example
Five: Perceptual grouping output for large-scale experiment using
RANSAC. |
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