Navigation Of Micro Aerial Vehicles Research Articles

Low computational-complexity algorithms for vision-aided inertial navigation of micro aerial vehicles

This paper presents low computational-complexity methods for micro-aerial-vehicle localization in GPS-denied environments. All the presented algorithms rely only on the data provided by a single onboard camera and an Inertial Measurement Unit (IMU). This paper deals with outlier rejection and relative-pose estimation. Regarding outlier rejection, we describe two methods. The former only requires the observation of a single feature in the scene and the knowledge of the angular rates from an IMU, under the assumption that the local camera motion lies in a plane perpendicular to the gravity vector. The latter requires the observation of at least two features, but it relaxes the hypothesis on the vehicle motion, being therefore suitable to tackle the outlier detection problem in the case of a 6DoF motion. We show also that if the camera is rigidly attached to the vehicle, motion priors from the IMU can be exploited to discard wrong estimations in the framework of a 2-point-RANSAC-based approach. Thanks to their inherent efficiency, the proposed methods are very suitable for resource-constrained systems. Regarding the pose estimation problem, we introduce a simple algorithm that computes the vehicle pose from the observation of three point features in a single camera image, once that the roll and pitch angles are estimated from IMU measurements. The proposed algorithm is based on the minimization of a cost function. The proposed method is very simple in terms of computational cost and, therefore, very suitable for real-time implementation. All the proposed methods are evaluated on both synthetic and real data.

Автоматическое распознавание зданий сложной формы при навигации микролетательных аппаратов

Автоматическое распознавание зданий сложной формы при навигации микролетательных аппаратов

Autonomous navigation and environment modeling for MAVs in 3-D enclosed industrial environments

In many applications, the industrial environments are typically 3-D indoor spaces enclosed by shell style structures, which are highly complex with known or unknown non-convex obstacles. GPS signal is unreliable or even unavailable inside, which poses significant technical challenges for the state estimation of micro aerial vehicles (MAVs) performing exploration and modeling tasks in such environments. In this paper, requirements and challenges for 3-D enclosed industrial environments exploration are analyzed firstly, and then state-of-art developments of MAV systems, environment modeling, visual navigation and guidance technologies are reviewed. A robust RGB-D odometry is introduced into the system to provide airborne 6-DOF state estimates of the MAV, which are fused with inertial measurements. Then the fused state information is used to assist the RGB-D based real time 3-D environment modeling. An improved closed-loop RRT based path planning approach (BI-RRT) is developed for information-efficient environment explorations. A flight experimental platform is constructed and the proposed system is validated in flight experiments.

Using natural features for vision based navigation of an indoor-VTOL MAV

The use of natural features for vision based navigation of an indoor Vertical-Take-Off-and-Landing (VTOL) Micro Aerial Vehicle (MAV) named Air-Quad is presented. Air-Quad is a small four-rotor helicopter developed at the ITE. Such a helicopter needs reliable attitude information. The measurements of the used MEMS gyroscopes and accelerometers are corrupted by strong noise. To be useful, the MEMS sensors have to be part of an integrated navigation system with aiding through complementary sensors like GPS or the computer vision module presented here. In the computer vision module, feature points are detected and tracked through the image sequence. The relative rotation and translation of the camera are estimated using the two-dimensional motion of the feature points. The three-dimensional points in the scene are modeled with the image coordinates of their first sighting and their inverse depths. Only these inverse depths are estimated for the feature points. An efficient sparse bundle adjustment algorithm is used to improve the estimation of the scene structure and the navigation solution. It is shown that the use of the computer vision module greatly improves the navigation solution compared to a solution based only on MEMS sensors.