Monocular 3D Exploration using Lines-of-Sight and Local Maps
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Monocular 3D Exploration using Lines-of-Sight and Local Maps Diego Pittol1
· Mathias Mantelli1
· Renan Maffei1
· Mariana Kolberg1
· Edson Prestes1
Received: 1 November 2019 / Accepted: 14 May 2020 © Springer Nature B.V. 2020
Abstract Nowadays, robots equipped with a single camera, such as micro aerial vehicles (MAVs), are easily found at affordable costs. They can be used in different tasks, including the building of 3D environment maps. For building such maps, monocular simultaneous localization and mapping (SLAM) methods are employed, which usually generate sparse or semi-dense representations that are ill-suited for navigation tasks. We propose a new 3D exploration approach that uses a monocular camera as the only source of information. Our approach transforms a point cloud generated by monocular SLAM into local volumetric maps. These maps are built using the lines-of-sight between points and keyframes, allowing the MAV to navigate safely through the environment. Goal poses are dynamically defined to guide the MAV to explore the environment while avoiding obstacles. Besides that, the proposed approach seeks to determine properly when the environment was entirely explored, preventing that MAV stops before cover all the environment or flies more that is needed. The effectiveness of the proposed approach is evaluated in experiments in two different indoor environments, and show that it is possible to explore an environment using only a MAV equipped with a single monocular camera. Keywords Robotics · Autonomous exploration · Monocular SLAM · Micro aerial vehicles
1 Introduction In recent years we have seen the dawning of a large number of applications that use autonomous robots in a wide range of areas, such as humanitarian [21, 32], military [2, 23] and civil applications. In many of them, the robot requires an environment map, which may not be available. Then, the robot must be able to build the map in an autonomous way, performing an exploration process. In SLAM methods, the map of the environment can be build in two possible ways: passively or actively. The difference is that in the active case the robot guides itself towards unknown regions, aiming to cover the whole environment, whereas in the passive case
The TITAN Xp used for this research was donated by the NVIDIA Corporation. This study was financed in part by the Brazilian National Council for Scientific and Technological Development (CNPq) and by the Coordenac¸a˜ o de Aperfeic¸oamento de Pessoal de N´ıvel Superior - Brasil (CAPES) - Finance Code 001. Diego Pittol
[email protected] 1
Institute of Informatics, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
the trajectory is defined by a human operator, and hence, the robot is not fully autonomous. This guiding process is the role of exploration and it is one of the fundamental problems in mobile robotics [29, 34]. Many exploration approaches were presented in the last decades, especially for ground robots equipped with rangefinder sensors [1, 14, 39, 43]. Nonetheless, recently
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