Browsing by Subject "Azimuth errors"

Now showing 1 - 2 of 2

Open Access
Differentiation and localization of target primitives using infrared sensors
(IEEE, 2002-09-10) Aytaç, Tayfun; Barshan, Billur
This study investigates the use of low-cost infrared sensors in the differentiation and localization of commonly encountered target primitives in indoor environments, such as planes, corners, edges, and cylinders. The intensity readings from such sensors are highly dependent on target location and properties in a way which cannot be represented in a simple manner, making the differentiation and localization process difficult. In this paper, we propose the use of angular intensity scans and present an algorithm to process them. This approach can determine the target type independent of its position. Once the target type is identified, its position can also be estimated. The method is verified experimentally. An average correct classification rate of 97% over all target types is achieved and targets are localized within absolute range and azimuth errors of 0.8 cm and 1.6°, respectively. The proposed method should facilitate the use of infrared sensors in mobile robot applications for differentiation and localization beyond their common usage as simple proximity sensors for object detection and collision avoidance.
Open Access
Simultaneous extraction of geometry and surface properties of targets using infrared intensity signals
(IEEE, 2005) Aytaç, Tayfun; Barshan, Billur
We propose the use of angular intensity signals obtained with low-cost infrared sensors and present an algorithm to simultaneously extract the geometry and surface properties of commonly encountered targets in indoor environments. The method is verified experimentally with planes, 90° corners, and 90° edges covered with aluminum, white cloth, and Styrofoam packaging material. An average correct classification rate of 80% of both geometry and surface over all target types is achieved and targets are localized within absolute range and azimuth errors of 1.5 cm and 1.1°, respectively. Taken separately, the geometry and surface type of targets can be correctly classified with rates of 99% and 81%, respectively, indicating that the geometrical properties of the targets are more distinctive than their surface properties, and surface determination is the limiting factor. The method demonstrated shows that simple infrared sensors, when coupled with appropriate signal processing, can be used to extract substantially more information than such devices are commonly employed for.