IEEE Robotics & Automation Magazine - September 2018 - 17

camera and the lidar scanner as well as the gravitational force
vector from the IMU sensor that is part of the MultiSense-SL
head. For the point cloud data, the pilot can select either to
accumulate the laser scanner data such that the whole environment is scanned or to use the filtered stereo RGB-D data. The
gravity vector is computed from the IMU data after passing a
Madgwick pose filtering in RT [21]. We analyzed the mean
and standard deviation IMU rotational error for the estimated
gravitational vector, which is 1.8° and 1.1°, respectively.
There are two options through the PI. First, the pilot can
select two seed points in the environment. For each seed
point, a local r-sphere neighborhood is searched in the point
cloud using a k-dimensional tree structure, where r is preselected by the pilot (in the experiments a sphere of 15-cm radius was used). For each neighborhood, a circular plane is fitted
using the RANSAC algorithm. The relative distances between
the two seed points and the perpendicular distances between
the fitted planes are computed as well as their relative angle,
i.e., the angle of their normal vectors. Second, the pilot can
compare the angle of the local fitted plane with the gravity
vector that is extracted from the IMU sensor. At the same
time, a 2-D map of the walls can be created using the simultaneous localization and mapping system introduced in [22], by
having the lidar scan rays parallel to the ground floor. An
example of these measurements can be seen in Figure 6.
Both modules are implemented in C++ as ROS nodes,
using the Point Cloud Library [23], whereas the second module works in RT and is part of the Surface Patch Library [24].
The thresholds and parameters setting for the filtering and
the plane estimations can be tweaked
dynamically through a graphical user
interface to meet specific demands
according to different environments.
The point cloud region, e.g., can be
limited to closer-to-robot points when
only planes around the robot are
required and not ceilings or floors.
Results and End-User Feedback
Figure 8 summarizes the indoor operations executed by the robot under the
supervision of the technical experts. In
detail it highlights the locations of the
various activities performed during
our field tests, like measurements and
manipulation tasks.
Measurements Acquisition
Figure 7(a) and (b) shows the 3-D
scene sent to the pilot PC1 and the
reconstructed planes computed by the
dedicated vision module for the first
explored room. Thanks to the acquired
measurements, it was possible to evaluate the state of the building. In particular, the representative engineering

and architecture professionals requested the assessment of the
wall inclination with respect to the ground. For the three
inspected rooms, the wall inclination with respect to the floor
was about 90° ( r/2 radians), and thus the building preserved
the structural integrity despite the copious earthquakes. Nevertheless, many cracks
were present in the building, and to evaluate the
The measurements
damage level, we were
requested to estimate the
are precise enough,
width and length of the
cracks. As shown in Figwithin 6 mm, to allow
ure 7(c)-(e), a set of cracks
were visible through the
the engineers and
PI. For those cracks, the
width estimation measurearchitects to assess the
ments were reported and
compared with the real
severity of the cracks.
crack size, which was measured manually on site.
The lidar sensor (Hokuyo UTM-30LX-EW) of the MultiSenseSL system was used for this purpose, given its high accuracy
compared to the other range sensors on the robot.
We tested the accuracy of the lidar point cloud by accumulating the point measurements on a plane and calculating the
average distance between two point neighbors (lateral accuracy) as well as the displacement depth of the same point during
some fixed time slot (depth accuracy). For surfaces 1 m from
the sensor the lateral accuracy is 6 mm and the depth

2
2

Planned Path

1 Room Number A Mission Target
Scan Spot

1 Crack Inspection

Figure 8. The robot is shown scanning rooms 1-4, measuring cracks, manipulating
objects, and opening a door during the field operations.

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IEEE ROBOTICS & AUTOMATION MAGAZINE

IEEE Robotics & Automation Magazine - September 2018

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