25 research outputs found
Nuclear Environments Inspection with Micro Aerial Vehicles: Algorithms and Experiments
In this work, we address the estimation, planning, control and mapping
problems to allow a small quadrotor to autonomously inspect the interior of
hazardous damaged nuclear sites. These algorithms run onboard on a
computationally limited CPU. We investigate the effect of varying illumination
on the system performance. To the best of our knowledge, this is the first
fully autonomous system of this size and scale applied to inspect the interior
of a full scale mock-up of a Primary Containment Vessel (PCV). The proposed
solution opens up new ways to inspect nuclear reactors and to support nuclear
decommissioning, which is well known to be a dangerous, long and tedious
process. Experimental results with varying illumination conditions show the
ability to navigate a full scale mock-up PCV pedestal and create a map of the
environment, while concurrently avoiding obstacles.Comment: 10 pages, ISER 201
GATSBI: An Online GTSP-Based Algorithm for Targeted Surface Bridge Inspection
We study the problem of visually inspecting the surface of a bridge using an
Unmanned Aerial Vehicle (UAV) for defects. We do not assume that the geometric
model of the bridge is known. The UAV is equipped with a LiDAR and RGB sensor
that is used to build a 3D semantic map of the environment. Our planner, termed
GATSBI, plans in an online fashion a path that is targeted towards inspecting
all points on the surface of the bridge. The input to GATSBI consists of a 3D
occupancy grid map of the part of the environment seen by the UAV so far. We
use semantic segmentation to segment the voxels into those that are part of the
bridge and the surroundings. Inspecting a bridge voxel requires the UAV to take
images from a desired viewing angle and distance. We then create a Generalized
Traveling Salesperson Problem (GTSP) instance to cluster candidate viewpoints
for inspecting the bridge voxels and use an off-the-shelf GTSP solver to find
the optimal path for the given instance. As more parts of the environment are
seen, we replan the path. We evaluate the performance of our algorithm through
high-fidelity simulations conducted in Gazebo. We compare the performance of
this algorithm with a frontier exploration algorithm. Our evaluation reveals
that targeting the inspection to only the segmented bridge voxels and planning
carefully using a GTSP solver leads to more efficient inspection than the
baseline algorithms.Comment: 8 pages, 16 figure
Stereo vision-based autonomous navigation for oil and gas pressure vessel inspection using a low-cost UAV
It is vital to visually inspect pressure vessels regularly in the oil and gas company to maintain their integrity. Compared with visual inspection conducted by sending engineers and ground vehicles into the pressure vessel, utilising an autonomous Unmanned Aerial Vehicle (UAV) can overcome many limitations including high labour intensity, low efficiency and high risk to human health. This work focuses on enhancing some existing technologies to support low-cost UAV autonomous navigation for visual inspection of oil and gas pressure vessels. The UAV can gain the ability to follow the planned trajectory autonomously to record videos with a stereo camera in the pressure vessel, which is a GPS-denied and low-illumination environment. Particularly, the ORB-SLAM3 is improved by adopting the image contrast enhancement technique to locate the UAV in this challenging scenario. What is more, a vision hybrid Proportional-Proportional-Integral-Derivative (P-PID) position tracking controller is integrated to control the movement of the UAV. The ROS-Gazebo-PX4 simulator is customised deeply to validate the developed stereo vision-based autonomous navigation approach. It is verified that compared with the ORB-SLAM3, the numbers of ORB feature points and effective matching points obtained by the improved ORB-SLAM3 are increased by more than 400% and 600%, respectively. Thereby, the improved ORB-SLAM3 is effective and robust enough for UAV self-localisation, and the developed stereo vision-based autonomous navigation approach can be deployed for pressure vessel visual inspectio