  This paper presents ORB-SLAM3, the first system able to perform visual,
visual-inertial and multi-map SLAM with monocular, stereo and RGB-D cameras,
using pin-hole and fisheye lens models. The first main novelty is a
feature-based tightly-integrated visual-inertial SLAM system that fully relies
on Maximum-a-Posteriori (MAP) estimation, even during the IMU initialization
phase. The result is a system that operates robustly in real-time, in small and
large, indoor and outdoor environments, and is 2 to 5 times more accurate than
previous approaches. The second main novelty is a multiple map system that
relies on a new place recognition method with improved recall. Thanks to it,
ORB-SLAM3 is able to 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 mapped areas. 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. This allows
to include in bundle adjustment co-visible keyframes, that provide high
parallax observations boosting accuracy, even if they are widely separated in
time or if they come from a previous mapping session. 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 3.6 cm on the EuRoC drone
and 9 mm under quick hand-held motions in the room of TUM-VI dataset, a setting
representative of AR/VR scenarios. For the benefit of the community we make
public the source code.
