Probabilistic three-dimensional object tracking based on adaptive depth segmentation

Object tracking is one of the fundamental topics of computer vision with diverse applications. The arising challenges in tracking, i.e., cluttered scenes, occlusion, complex motion, and illumination variations have motivated utilization of depth information from 3D sensors. However, current 3D trackers are not applicable to unconstrained environments without a priori knowledge. As an important object detection module in tracking, segmentation subdivides an image into its constituent regions. Nevertheless, the existing range segmentation methods in literature are difficult to implement in real-time due to their slow performance. In this thesis, a 3D object tracking method based on adaptive depth segmentation and particle filtering is presented. In this approach, the segmentation method as the bottom-up process is combined with the particle filter as the top-down process to achieve efficient tracking results under challenging circumstances. The experimental results demonstrate the efficiency, as well as robustness of the tracking algorithm utilizing real-world range information

A new straight line reconstruction methodology from multi-spectral stereo aerial images

In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction

Plan-view Trajectory Estimation with Dense Stereo Background Models

In a known environment, objects may be tracked in multiple views using a set of back-ground models. Stereo-based models can be illumination-invariant, but often have undefined values which inevitably lead to foreground classification errors. We derive dense stereo models for object tracking using long-term, extended dynamic-range imagery, and by detecting and interpolating uniform but unoccluded planar regions. Foreground points are detected quickly in new images using pruned disparity search. We adopt a 'late-segmentation' strategy, using an integrated plan-view density representation. Foreground points are segmented into object regions only when a trajectory is finally estimated, using a dynamic programming-based method. Object entry and exit are optimally determined and are not restricted to special spatial zones

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Implementing Simultaneous Localization and Mapping

This paper will provide an overview of existing SLAM techniques and a brief review of several implementations. This will be done by describing the SLAM problem formulation, followed by the details involved in implementing both the image recognition and motion estimation approaches. The application of SLAM using other sensors, such as radar, will also be discussed

Integration of 3D vision based structure estimation and visual robot control

Enabling robot manipulators to manipulate and/or recognise arbitrarily placed 3D objects under sensory control is one of the key issues in robotics. Such robot sensors should be capable of providing 3D information about objects in order to accomplish the above mentioned tasks. Such robot sensors should also provide the means for multisensor or multimeasurement integration. Finally, such 3D information should be efficiently used for performing desired tasks. This work develops a novel computational frame wo rk for solving some of these problems. A vision (camera) sensor is used in conjunction with a robot manipulator, in the frame-work of active vision to estimate 3D structure (3D geometrical model) of a class of objects. Such information is used for the visual robot control, in the frame-work of model based vision. One part o f this dissertation is devoted to the system calibration. The camera and eye/hand calibration is presented. Several contributions are introduced in this part, intended to improve existing calibration procedures. This results in more efficient and accurate calibrations. Experimental results are presented. Second part of this work is devoted to the methods of image processing and image representation. Methods for extracting and representing necessary image features comprising vision based measurements are given. Third part of this dissertation is devoted to the 3D geometrical model reconstruction of a class o f objects (polyhedral objects). A new technique for 3D model reconstruction from an image sequence is introduced. This algorithm estimates a 3D model of an object in terms of 3D straight-line segments (wire-frame model) by integrating pertinent information over an image sequence. The image sequence is obtained from a moving camera mounted on a robot arm. Experimental results are presented. Fourth part of this dissertation is devoted to the robot visual control. A new visual control strategy is introduced. In particular, the necessary homogeneous transformation matrix for the robot gripper in order to grasp an arbitrarily placed 3D object is estimated. This problem is posed as a problem of 3D displacement (motion) estimation between the reference model of an object and the actual model of the object. Further, the basic algorithm is extended to handle multiple object manipulation and recognition. Experimental results are presented

A Neural Model of How the Brain Computes Heading from Optic Flow in Realistic Scenes

Animals avoid obstacles and approach goals in novel cluttered environments using visual information, notably optic flow, to compute heading, or direction of travel, with respect to objects in the environment. We present a neural model of how heading is computed that describes interactions among neurons in several visual areas of the primate magnocellular pathway, from retina through V1, MT+, and MSTd. The model produces outputs which are qualitatively and quantitatively similar to human heading estimation data in response to complex natural scenes. The model estimates heading to within 1.5° in random dot or photo-realistically rendered scenes and within 3° in video streams from driving in real-world environments. Simulated rotations of less than 1 degree per second do not affect model performance, but faster simulated rotation rates deteriorate performance, as in humans. The model is part of a larger navigational system that identifies and tracks objects while navigating in cluttered environments.National Science Foundation (SBE-0354378, BCS-0235398); Office of Naval Research (N00014-01-1-0624); National-Geospatial Intelligence Agency (NMA201-01-1-2016