Visual Tracking Using Sparse Coding and Earth Mover's Distance

An efficient iterative Earth Mover's Distance (iEMD) algorithm for visual tracking is proposed in this paper. The Earth Mover's Distance (EMD) is used as the similarity measure to search for the optimal template candidates in feature-spatial space in a video sequence. The computation of the EMD is formulated as the transportation problem from linear programming. The efficiency of the EMD optimization problem limits its use for visual tracking. To alleviate this problem, a transportation-simplex method is used for EMD optimization and a monotonically convergent iterative optimization algorithm is developed. The local sparse representation is used as the appearance models for the iEMD tracker. The maximum-alignment-pooling method is used for constructing a sparse coding histogram which reduces the computational complexity of the EMD optimization. The template update algorithm based on the EMD is also presented. The iEMD tracking algorithm assumes small inter-frame movement in order to guarantee convergence. When the camera is mounted on a moving robot, e.g., a flying quadcopter, the camera could experience a sudden and rapid motion leading to large inter-frame movements. To ensure that the tracking algorithm converges, a gyro-aided extension of the iEMD tracker is presented, where synchronized gyroscope information is utilized to compensate for the rotation of the camera. The iEMD algorithm's performance is evaluated using eight publicly available datasets. The performance of the iEMD algorithm is compared with seven state-of-the-art tracking algorithms based on relative percentage overlap. The robustness of this algorithm for large inter-frame displacements is also illustrated

Monocular-Based Pose Determination of Uncooperative Known and Unknown Space Objects

In order to support spacecraft proximity operations, such as on-orbit servicing and spacecraft formation flying, several vision-based techniques exist to determine the relative pose of an uncooperative orbiting object with respect to the spacecraft. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously by making use of only optical measurements. In this paper, we investigate two vision-based approaches for pose estimation of uncooperative orbiting targets: one that is general and versatile such that it does not require a priori knowledge of any information of the target, and the other one that requires knowledge of the target's shape geometry. The former uses an estimation algorithm of translational and rotational dynamics to sequentially perform simultaneous pose determination and 3D shape reconstruction of the unknown target, while the latter relies on a known 3D model of the target's geometry to provide a point-by-point pose solution. The architecture and implementation of both methods are presented and their achievable performance is evaluated through numerical simulations. In addition, a computer vision processing strategy for feature detection and matching and the Structure from Motion (SfM) algorithm for on-board 3D reconstruction are also discussed and validated by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Monocular-Based Pose Determination of Uncooperative Space Objects

Vision-based methods to determine the relative pose of an uncooperative orbiting object are investigated in applications to spacecraft proximity operations, such as on-orbit servicing, spacecraft formation flying, and small bodies exploration. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously in real-time by making use of only optical measurements. The Simultaneous Estimation of Pose and Shape (SEPS) algorithm that does not require a priori knowledge of the pose and shape of the target is presented. This makes use of a novel measurement equation and filter that can efficiently use optical flow information along with a star tracker to estimate the target's angular rotational and translational relative velocity as well as its center of gravity. Depending on the mission constraints, SEPS can be augmented by a more accurate offline, on-board 3D reconstruction of the target shape, which allows for the estimation of the pose as a known target. The use of Structure from Motion (SfM) for this purpose is discussed. A model-based approach for pose estimation of known targets is also presented. The architecture and implementation of both the proposed approaches are elucidated and their performance metrics are evaluated through numerical simulations by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Motion primitives and 3D path planning for fast flight through a forest

This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives

High level 3D structure extraction from a single image using a CNN-based approach

High-Level Structure (HLS) extraction in a set of images consists of recognizing 3D elements with useful information to the user or application. There are several approaches to HLS extraction. However, most of these approaches are based on processing two or more images captured from different camera views or on processing 3D data in the form of point clouds extracted from the camera images. In contrast and motivated by the extensive work developed for the problem of depth estimation in a single image, where parallax constraints are not required, in this work, we propose a novel methodology towards HLS extraction from a single image with promising results. For that, our method has four steps. First, we use a CNN to predict the depth for a single image. Second, we propose a region-wise analysis to refine depth estimates. Third, we introduce a graph analysis to segment the depth in semantic orientations aiming at identifying potential HLS. Finally, the depth sections are provided to a new CNN architecture that predicts HLS in the shape of cubes and rectangular parallelepipeds

Monocular-Based Pose Determination of Uncooperative Known and Unknown Space Objects

In order to support spacecraft proximity operations, such as on-orbit servicing and spacecraft formation flying, several vision-based techniques exist to determine the relative pose of an uncooperative orbiting object with respect to the spacecraft. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously by making use of only optical measurements. In this paper, we investigate two vision-based approaches for pose estimation of uncooperative orbiting targets: one that is general and versatile such that it does not require a priori knowledge of any information of the target, and the other one that requires knowledge of the target's shape geometry. The former uses an estimation algorithm of translational and rotational dynamics to sequentially perform simultaneous pose determination and 3D shape reconstruction of the unknown target, while the latter relies on a known 3D model of the target's geometry to provide a point-by-point pose solution. The architecture and implementation of both methods are presented and their achievable performance is evaluated through numerical simulations. In addition, a computer vision processing strategy for feature detection and matching and the Structure from Motion (SfM) algorithm for on-board 3D reconstruction are also discussed and validated by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Monocular-Based Pose Determination of Uncooperative Space Objects

Vision-based methods to determine the relative pose of an uncooperative orbiting object are investigated in applications to spacecraft proximity operations, such as on-orbit servicing, spacecraft formation flying, and small bodies exploration. Depending on whether the object is known or unknown, a shape model of the orbiting target object may have to be constructed autonomously in real-time by making use of only optical measurements. The Simultaneous Estimation of Pose and Shape (SEPS) algorithm that does not require a priori knowledge of the pose and shape of the target is presented. This makes use of a novel measurement equation and filter that can efficiently use optical flow information along with a star tracker to estimate the target's angular rotational and translational relative velocity as well as its center of gravity. Depending on the mission constraints, SEPS can be augmented by a more accurate offline, on-board 3D reconstruction of the target shape, which allows for the estimation of the pose as a known target. The use of Structure from Motion (SfM) for this purpose is discussed. A model-based approach for pose estimation of known targets is also presented. The architecture and implementation of both the proposed approaches are elucidated and their performance metrics are evaluated through numerical simulations by using a dataset of images that are synthetically generated according to a chaser/target relative motion in Geosynchronous Orbit (GEO)

Motion primitives and 3D path planning for fast flight through a forest

This paper presents two families of motion primitives for enabling fast, agile flight through a dense obstacle field. The first family of primitives consists of a time-delay dependent 3D circular path between two points in space and the control inputs required to fly the path. In particular, the control inputs are calculated using algebraic equations which depend on the flight parameters and the location of the waypoint. Moreover, the transition between successive maneuver states, where each state is defined by a unique combination of constant control inputs, is modeled rigorously as an instantaneous switch between the two maneuver states following a time delay which is directly related to the agility of the robotic aircraft. The second family consists of aggressive turn-around (ATA) maneuvers which the robot uses to retreat from impenetrable pockets of obstacles. The ATA maneuver consists of an orchestrated sequence of three sets of constant control inputs. The duration of the first segment is used to optimize the ATA for the spatial constraints imposed by the turning volume. The motion primitives are validated experimentally and implemented in a simulated receding horizon control (RHC)-based motion planner. The paper concludes with inverse-design pointers derived from the primitives