Intelligent sensing for robot mapping and simultaneous human localization and activity recognition
Author(s)
Advisor
Barshan, BillurDate
2011Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
We consider three different problems in two different sensing domains, namely
ultrasonic sensing and inertial sensing. Since the applications considered in each
domain are inherently different, this thesis is composed of two main parts. The
approach common to the two parts is that raw data acquired from simple sensors
is processed intelligently to extract useful information about the environment.
In the first part, we employ active snake contours and Kohonen’s selforganizing
feature maps (SOMs) for representing and evaluating discrete point
maps of indoor environments efficiently and compactly. We develop a generic
error criterion for comparing two different sets of points based on the Euclidean
distance measure. The point sets can be chosen as (i) two different sets of map
points acquired with different mapping techniques or different sensing modalities,
(ii) two sets of fitted curve points to maps extracted by different mapping techniques
or sensing modalities, or (iii) a set of extracted map points and a set of
fitted curve points. The error criterion makes it possible to compare the accuracy
of maps obtained with different techniques among themselves, as well as with an
absolute reference. We optimize the parameters of active snake contours and
SOMs using uniform sampling of the parameter space and particle swarm optimization.
A demonstrative example from ultrasonic mapping is given based on
experimental data and compared with a very accurate laser map, considered an
absolute reference. Both techniques can fill the erroneous gaps in discrete point
maps. Snake curve fitting results in more accurate maps than SOMs because it is
more robust to outliers. The two methods and the error criterion are sufficiently
general that they can also be applied to discrete point maps acquired with other
mapping techniques and other sensing modalities.
In the second part, we use body-worn inertial/magnetic sensor units for recognition
of daily and sports activities, as well as for human localization in GPSdenied
environments. Each sensor unit comprises a tri-axial gyroscope, a tri-axial
accelerometer, and a tri-axial magnetometer. The error characteristics of the sensors
are modeled using the Allan variance technique, and the parameters of lowand
high-frequency error components are estimated.
Then, we provide a comparative study on the different techniques of classifying
human activities that are performed using body-worn miniature inertial and
magnetic sensors. Human activities are classified using five sensor units worn
on the chest, the arms, and the legs. We compute a large number of features
extracted from the sensor data, and reduce these features using both Principal
Components Analysis (PCA) and sequential forward feature selection (SFFS).
We consider eight different pattern recognition techniques and provide a comparison
in terms of the correct classification rates, computational costs, and their
training and storage requirements. Results with sensors mounted on various locations
on the body are also provided. The results indicate that if the system
is trained by the data of an individual person, it is possible to obtain over 99%
correct classification rates with a simple quadratic classifier such as the Bayesian
decision method. However, if the training data of that person are not available
beforehand, one has to resort to more complex classifiers with an expected correct
classification rate of about 85%.
We also consider the human localization problem using body-worn inertial/
magnetic sensors. Inertial sensors are characterized by drift error caused
by the integration of their rate output to get position information. Because of
this drift, the position and orientation data obtained from inertial sensor signals
are reliable over only short periods of time. Therefore, position updates from externally
referenced sensors are essential. However, if the map of the environment
is known, the activity context of the user provides information about position. In
particular, the switches in the activity context correspond to discrete locations
on the map. By performing activity recognition simultaneously with localization,
one can detect the activity context switches and use the corresponding position
information as position updates in the localization filter. The localization filter
also involves a smoother, which combines the two estimates obtained by running
the zero-velocity update (ZUPT) algorithm both forward and backward in time.
We performed experiments with eight subjects in an indoor and an outdoor environment
involving “walking,” “turning,” and “standing” activities. Using the
error criterion in the first part of the thesis, we show that the position errors can
be decreased by about 85% on the average. We also present the results of a 3-D
experiment performed in a realistic indoor environment and demonstrate that it
is possible to achieve over 90% error reduction in position by performing activity
recognition simultaneously with localization.
Keywords
Intelligent sensingultrasonic sensing
inertial sensing
robot mapping
sensor error modeling
pattern recognition
wearable computing
human localization
human activity recognition
simultaneous human localization and activity recognition