Visual-model-based, real-time 3D pose tracking for autonomous navigation: methodology and experiments

Abstract

This paper presents a novel 3D-model-based computer-vision method for tracking the full six degreeof-freedom (dof) pose (position and orientation) of a rigid body, in real-time. The methodology has been targeted for autonomous navigation tasks, such as interception of or rendezvous with mobile targets. Tracking an object's complete six-dof pose makes the proposed algorithm useful even when targets are not restricted to planar motion (e.g., flying or rough-terrain navigation). Tracking is achieved via a combination of textured model projection and optical flow. The main contribution of our work is the novel combination of optical flow with z-buffer depth information that is produced during model projection. This allows us to achieve six-dof tracking with a single camera.A localized illumination normalization filter also has been developed in order to improve robustness to shading. Real-time operation is achieved using GPU-based filters and a new data-reduction algorithm based on colour-gradient redundancy, which was developed within the framework of our project. Colour-gradient redundancy is an important property of colour images, namely, that the gradients of all colour channels are generally aligned. Exploiting this property provides a threefold increase in speed. A processing rate of approximately 80 to 100 fps has been obtained in our work when utilizing synthetic and real target-motion sequences. Sub-pixel accuracies were obtained in tests performed under different lighting conditions.

Model predictive cooperative localization control of multiple UAVs using potential function sensor constraints

“…In the context of formation control the problem of a limited sensor perception space is critical along with collision avoidance and trajectory tracking. One approach for vision sensors is visual servoing based on optical flow or features, as shown in de Ruiter and Benhabib (2008), Kendoul et al (2009) and Erkent and Işıl Bozma (2012). For traditional backstepping controllers, sensor perception limits are addressed by switching the robot's formation control according to the compliance with the sensor constraints (Wang et al 2015).…”

Advances in real-time object tracking

“…The approach described by [23] uses a modified version of the Active Appearance Model which allows for partial and self occlusion of the objects and for high accuracy and precision. Minimize the optical flow resulting from the projection of a textured model and the camera image [31]. To compensate for shadows and changing lighting, they apply an illumination normalisation technique.…”

“…Iterative particle filtering As proposed in previous works by [24,31], iterative particle filtering increases responsiveness to rapid pose changes. Therefore, steps 2 and 3 of Algorithm 1 are performed several times on the same image.…”

Navigation Algorithm Applied to the Bit of Underwater Horizontal Directional Drilling in a Vibration Environment

“…In an underwater environment, it is not possible to use laser sensors (Yang et al 2011) and there are also many difficulties in using vision sensors (de Ruiter and Benhabib 2008;Stelzer et al 2012) or the global positioning system (Sahawneh et al 2011;Liang and Jia 2015;Chiang et al 2011;Leung et al 2011;Chiang and Huang 2008) because of lighting conditions and signal interference. Therefore, ultra-short baseline (USBL), Doppler velocity log, and sonar have been widely applied in navigation of underwater vehicles (Lee and Jun 2007;Li et al 2014Li et al , 2015Allotta et al 2014Allotta et al , 2015Allotta et al , 2016Morgado et al 2011;He et al 2015).…”