Abstract
This paper presents a strategy to autonomously explore unknown indoor environments, focusing on 3D mapping of the environment and performing grid level semantic labeling to identify all available objects. Unlike conventional exploration techniques that utilize geometric heuristics and information gain theory on an occupancy grid map, the work presented in this paper considers semantic information, such as the class of objects, in order to gear the exploration towards environmental segmentation and object labeling. The proposed approach utilizes deep learning to map 2D semantically segmented images into 3D semantic point clouds that encapsulate both occupancy and semantic annotations. A next-best-view exploration algorithm is employed to iteratively explore and label all the objects in the environment using a novel utility function that balances exploration and semantic object labeling. The proposed strategy was evaluated in a realistically simulated indoor environment, and results were benchmarked against other exploration strategies.
Highlights
The growth in aerial robotics has led to their ubiquitous presence in various fields—urban search and rescue (USAR) [1,2,3], infrastructure inspection [4], surveillance [5], etc
Some recent research has focused on USAR activities performed by unmanned aerial vehicles (UAVs) to assist rescue teams by providing vital information on time-sensitive situations without endangering human lives
UAVs’ speed, agility, and ability to navigate in hazardous environments that contain rubble and obstacles make them an ideal platform for deployment in USAR environments
Summary
The growth in aerial robotics has led to their ubiquitous presence in various fields—urban search and rescue (USAR) [1,2,3], infrastructure inspection [4], surveillance [5], etc. Some recent research has focused on USAR activities performed by unmanned aerial vehicles (UAVs) to assist rescue teams by providing vital information on time-sensitive situations without endangering human lives. The introduction of unmanned aerial vehicles (UAVs) in USAR environments is beneficial when rapid responses are required to assist first responders with locating victims, survivors, and danger sources in the environment. UAVs with autonomous exploration, mapping, and navigation capabilities can provide rescuers with valuable data, such as 2D and 3D maps, victims’ locations, and localized danger sources, in order to improve their awareness of the situation and respond appropriately. Providing a rich/informative map for the first responders in USAR with the right representation is an active topic in the USAR robotics field
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