4DoF drift free navigation using inertial cues and optical flow

Abstract

In this paper, we describe a novel approach in fusing optical flow with inertial cues (3D acceleration and 3D angular velocities) in order to navigate a Micro Aerial Vehicle (MAV) drift free in 4DoF and metric velocity. Our approach only requires two consecutive images with a minimum of three feature matches. It does not require any (point) map nor any type of feature history. Thus it is an inherently failsafe approach that is immune to map and feature-track failures. With these minimal requirements we show in real experiments that the system is able to navigate drift free in all angles including yaw, in one metric position axis, and in 3D metric velocity. Furthermore, it is a power-on-and-go system able to online selfcalibrate the inertial biases, the visual scale and the full 6DoF extrinsic transformation parameters between camera and IMU.

Computer Vision for Micro Air Vehicles

“…This inertial-optical flow (IOF)-based approach does not use any kind of history that can be corrupted and does not require to find the same features in later frames. In [66], we show that we still can estimate the metric velocity of the MAV, its metric distance to the scene, and its full attitude (roll, pitch, yaw) drift free while maintaining a self-calibrating system. That is, in addition to the states used for control, we can estimate the IMU biases and the camera-IMU extrinsics, and do not need specific calibration steps prior to launch.…”

“…We showed in [66] that we can control the MAV with IOF drift free in metric velocity, full attitude, and metric scene distance. Being able to keep the MAV constant in heading and scene distance is crucial for automatic initialization of more powerful algorithms (e.g., VSLAM) to control the vehicle in full 6DoF pose.…”

“…In [65,66], we present an approach which only uses two consecutive camera images and inertial cues for MAV navigation. This inertial-optical flow (IOF)-based approach does not use any kind of history that can be corrupted and does not require to find the same features in later frames.…”

Planar-Based Visual Inertial Navigation: Observability Analysis and Motion Estimation

“…Although the observation of ground planar features has been implicitly used in visual inertial navigation systems [2,11,22,25,34,35,37,40], this condition was first explicitly used in [30] for the VINS motion estimation. In [30], we proposed an IMU-camera ego-motion approach in which the ground planar features were directly used to construct the inertial model of the system.…”

Inertial-aided state and slope estimation using a monocular camera

“…A prediction scheme is proposed in [12], where the image is first warped by the relative rotation between two frames thanks to the gyroscopes, then the OF algorithm is more likely to converge. Another important application of inertial sensors is in recovering the visual scale factor of a monocular camera [13], [14]. The observability and intersensor calibration of the coupled system is discussed in [15].…”