  This paper presents fast procedures for thermal infrared remote sensing in
dark, GPS-denied environments, such as those found in industrial plants such as
in High-Voltage Direct Current (HVDC) converter stations. These procedures are
based on the combination of the depth estimation obtained from either a
1-Dimensional LIDAR laser or a 2-Dimensional Hokuyo laser or a 3D MultiSense
SLB laser sensor and the visible and thermal cameras from a FLIR Duo R
dual-sensor thermal camera. The combination of these sensors/cameras is
suitable to be mounted on Unmanned Aerial Vehicles (UAVs) and/or robots in
order to provide reliable information about the potential malfunctions, which
can be found within the hazardous environment. For example, the capabilities of
the developed software and hardware system corresponding to the combination of
the 1-D LIDAR sensor and the FLIR Duo R dual-sensor thermal camera is assessed
from the point of the accuracy of results and the required computational times:
the obtained computational times are under 10 ms, with a maximum localization
error of 8 mm and an average standard deviation for the measured temperatures
of 1.11 degree Celsius, which results are obtained for a number of test cases.
The paper is structured as follows: the description of the system used for
identification and localization of hotspots in industrial plants is presented
in section II. In section III, the method for faults identification and
localization in plants by using a 1-Dimensional LIDAR laser sensor and thermal
images is described together with results. In section IV the real time thermal
image processing is presented. Fusion of the 2-Dimensional depth laser Hokuyo
and the thermal images is described in section V. In section VI the combination
of the 3D MultiSense SLB laser and thermal images is described. In section VII
a discussion and several conclusions are drawn.

Robotics;Computer Vision and Pattern Recognition;Systems and Control;Systems and Control