Computationally efficient visual–inertial sensor fusion for Global Positioning System–denied navigation on a small quadrotor

Abstract

Because of the complementary nature of visual and inertial sensors, the combination of both is able to provide fast and accurate 6 degree-of-freedom state estimation, which is the fundamental requirement for robotic (especially, unmanned aerial vehicle) navigation tasks in Global Positioning System-denied environments. This article presents a computationally efficient visual-inertial fusion algorithm, by separating orientation fusion from the position fusion process. The algorithm is designed to perform 6 degree-of-freedom state estimation, based on a gyroscope, an accelerometer and a monocular visual-based simultaneous localisation and mapping algorithm measurement. It also recovers the visual scale for the monocular visual-based simultaneous localisation and mapping. In particular, the fusion algorithm treats the orientation fusion and position fusion as two separate processes, where the orientation fusion is based on a very efficient gradient descent algorithm, whereas the position fusion is based on a 13-state linear Kalman filter. The elimination of the magnetometer sensor avoids the problem of magnetic distortion, which makes it a power-on-and-go system once the accelerometer is factory calibrated. The resulting algorithm shows a significant computational reduction over the conventional extended Kalman filter, with competitive accuracy. Moreover, the separation between orientation and position fusion processes enables the algorithm to be easily implemented onto two individual hardware elements and thus allows the two fusion processes to be executed concurrently.

Adaptive Absolute Ego-Motion Estimation Using Wearable Visual-Inertial Sensors for Indoor Positioning

“…Although [ 27 ] proposed a modified linear Kalman filter to perform the fusion of inertial and visual data, the accurate orientation estimates were based on the assumption of gyroscope measurements trusted for up to several minutes. In [ 28 ], the authors proposed a novel fusion algorithm by separating the orientation fusion and the position fusion process, while the orientation estimation could only be robust for a static or slow movement without magnetic distortions using the method proposed in [ 29 ]. In contrast, in this paper, the orientation is firstly estimated by our previously proposed orientation filter in [ 2 ] only from inertial measurements.…”

Time-of-Travel Methods for Measuring Optical Flow on Board a Micro Flying Robot

“…Micro cameras have been proposed for MAV applications (e.g., a PAL (PAL stands for Phase Alternating Line) -camera with 720 × 576 pixels at 25 fps [16] or a CMOS (CMOS stands for Complementary Metal-Oxide Semiconductor) camera with 752 × 480 pixels at 80 fps [19]), including visual-based simultaneous localization and mapping algorithms (SLAM).…”