This article presents ORB-SLAM3, the first system able to perform visual, visual-inertial and multimap SLAM with monocular, stereo and RGB-D cameras, using pin-hole and fisheye lens models. The first main novelty is a tightly integrated visualinertial SLAM system that fully relies on maximum a posteriori (MAP) estimation, even during IMU initialization, resulting in real-time robust operation in small and large, indoor and outdoor environments, being two to ten times more accurate than previous approaches. The second main novelty is a multiple map system relying on a new place recognition method with improved recall that lets ORB-SLAM3 survive to long periods of poor visual information: when it gets lost, it starts a new map that will be seamlessly merged with previous maps when revisiting them. Compared with visual odometry systems that only use information from the last few seconds, ORB-SLAM3 is the first system able to reuse in all the algorithm stages all previous information from high parallax co-visible keyframes, even if they are widely separated in time or come from previous mapping sessions, boosting accuracy. Our experiments show that, in all sensor configurations, ORB-SLAM3 is as robust as the best systems available in the literature and significantly more accurate. Notably, our stereo-inertial SLAM achieves an average accuracy of 3.5 cm in the EuRoC drone and 9 mm under quick hand-held motions in the room of TUM-VI dataset, representative of AR/VR scenarios. For the benefit of the community we make public the source code.

This paper describes the development of an aerial system applied for the terrain mapping and geo-detection of explosive landmine-like objects. In practice in Colombia, a large percentage of the anti-personnel mines that still remain across the country are hand-crafted and partially exposed on the terrain's surface so that they can be triggered. This scenario facilitates the use of a vision-based approach for the detection of these artifacts. Our goal is to integrate computer vision algorithms within a low-cost UAV robot suited for the Colombian scenario. The aerial system enables: (i) terrain mapping based on a visual stitching method to generate a mosaic image of the covered terrain, and (ii) the visual detection of landmine-like objects in real-time. Despite the hardware drawbacks and the camera limitations of the used UAV, we demonstrate that our low-cost platform could be used as a complementary tool for demining missions in Colombia. After briefly reviewing the state of the art regarding the use of robots for mine clearance, we present a field report that confirms the feasibility of our aerial-based system featuring in approximate scenarios.

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