Robust embedded egomotion estimation

Abstract

Abstract-This work presents a method for estimating the egomotion of an aerial vehicle in challenging industrial environments. It combines binocular visual and inertial cues in a tightly-coupled fashion and operates in real time on an embedded platform. An extended Kalman filter fuses measurements and makes motion estimation rely more on inertial data if visual feature constellation is degenerate. Errors in roll and pitch are bounded implicitly by the gravity vector. Inertial sensors are used for efficient outlier detection and enable operation in poorly and repetitively textured environments. We demonstrate robustness and accuracy in an industrial scenario as well as in general indoor environments. The former is accompanied by a detailed performance evaluation supported with ground truth measurements from an external tracking system.

A UAV system for inspection of industrial facilities

“…Fusion of visual and inertial cues is, strictly speaking, implemented in a loosely coupled fashion following the stochastic cloning approach outlined in [18]. Only an overview of the processing pipeline is provided here, and the interested reader is referred to [19] for further details.…”

“…We applied a loosely-coupled approach for visual-inertial state estimation [19] running on-board the aerial vehicle: this algorithm serves as the basis for the model-predictive controller described above. As a complementary method, we also experimented with tight integration of inertial sensing and visual odometry.…”

Invariant Observer-Based State Estimation for Micro-Aerial Vehicles in GPS-Denied Indoor Environments Using an RGB-D Camera and MEMS Inertial Sensors

“…The EKF sensor fusion filter combines relative motion estimates from the stereo visual odometry with IMU measurements, and periodically incorporates position corrections from a SLAM module to bound the drift. More recently, Voigt et al [16] proposed an EKF-based visual-inertial ego-motion estimation method that combines stereo vision and IMU measurements in a tightly coupled manner. The resulting scheme utilizes IMU state and covariance propagation information to aid the feature matching of the stereo vision, leading to increased efficiency and robustness of MAV state estimation in complex industrial environments.…”

“…The centroids ( , p q ) of two point clouds are computed first, and the relative distances (∆pi, ∆qi) are then calculated according to Equation (16). Given ∆pi, ∆qi, the algorithm computes the following matrix:…”

“…Moreover, they are unable to fully utilize the information in a 3D environment. In addition, both monocular [11][12][13] and stereo vision [14][15][16] technologies based on onboard cameras have also been employed to provide relative estimates for indoor navigation of MAVs. Although appealing in many applications, the monocular and stereo vision based approaches have several drawbacks in addressing indoor MAV navigation problems: Conventional RGB cameras do not directly provide distance information of environments, thus monocular/stereo vision based approaches must calculate the depth data using image features, which is a computationally intensive process.…”