Xavier Bouyssounouse

Autonomous indoor object tracking with the Parrot AR.Drone

This article presents an image-based visual servoing system for indoor visual tracking of 3D moving objects by an Unmanned Aerial Vehicle. This system autonomously follows a 3D moving target object, maintaining it with a fixed distance and centered on its image plane. The initial setup is tested in a detailed simulation environment. The system is then validated on flights in indoor scenarios using the Parrot AR.Drone and the CMT tracker, demonstrating the robustness of the system to differences in object features, environmental clutter, and target trajectory. The obtained results indicate that the proposed system is suitable for complex controls task, such object surveillance and pursuit.

Planetary rover localization within orbital maps

This paper introduces an advanced rover localization system suitable for autonomous planetary exploration in the absence of Global Positioning System (GPS) infrastructure. Given an existing terrain map (image and elevation) obtained from satellite imagery and the images provided by the rover stereo camera system, the proposed method determines the best rover location through visual odometry, 3D terrain and horizon matching. The system is tested on data retrieved from a 3 km traverse of the Basalt Hills quarry in California where the GPS track is used as ground truth. Experimental results show the system presented here reduces by over 60% the localization error obtained by wheel odometry.

An Integrated System for Autonomous Search and Track with a small Unmanned Aerial Vehicle

This article presents a framework for searching, detecting, and tracking an object of interest with a small unmanned aerial vehicle (sUAV). The vehicle is given an area to search for an object of interest. Once the object is detected, the sUAV follows the target while maintaining a fixed distance and centered on its image plane. This paper describes an architecture for integrating autonomy capabilities for search and track for sUAVs. The system has been implemented in the Robot Operating System (ROS) framework using the Parrot ARDrone platform. In this paper we present our technical approach, the system architecture and demonstrate the principles of the approach in a simulation environment and indoor flight tests.

Horizon based orientation estimation for planetary surface navigation

Planetary rovers navigate in extreme environments for which a Global Positioning System (GPS) is unavailable, maps are restricted to relatively low resolution provided by orbital imagery, and compass information is often lacking due to weak or not existent magnetic fields. However, an accurate rover localization is particularly important to achieve the mission success by reaching the science targets, avoiding negative obstacles visible only in orbital maps, and maintaining good communication connections with ground. This paper describes a horizon solution for precise rover orientation estimation. The detected horizon in imagery provided by the on board navigation cameras is matched with the horizon rendered over the existing terrain model. The set of rotation parameters (roll, pitch yaw) that minimize the cost function between the two horizon curves corresponds to the rover estimated pose.