Locating and tracking objects by efficient comparison of real and predicted synthetic video imagery

Abstract

A mobile robot moving in an environment in which there are other moving objects and active agents, some of which may represent threats and some of which may represent collaborators, needs to be able to reason about the potential future behaviors of those objects and agents. In this paper we present an approach to tracking targets with complex behavior, leveraging a 3D simulation engine to generate predicted imagery and comparing that against real imagery. We introduce an approach to compare real and simulated imagery and present results using this approach to locate and track objects with complex behaviors.In this approach, the salient points in real and imaged images are identified and an affine image transformation that maps the real scene to the synthetic scene is generated. An image difference operation is developed that ensures that the matched points in both images produce a zero difference. In this way, synchronization differences are reduced and content differences enhanced. A number of image pairs are processed and presented to illustrate the approach.

Navigation of uncertain terrain by fusion of information from real and synthetic imagery

“…ADAPT merges RS [11], a language for specifying and reasoning about sensory-based robot plans with SOAR [9] a widely used cognitive architecture. The visual component of the perceptual apparatus for ADAPT, introduced in [10] and developed in [12] [13], leverages the state of the art in physics-engines for video gaming to provide a fast and effective 3D simulation system. A physics-engine is a module used in video game software to ensure that the virtual game world obeys the laws of physics and looks realistic to the game player.…”

“…The images from the robot camera I r and simulation camera I s are fused using the MMD module [10]. The first output of the MMD operation is the image homography that maps between the real and synthetic images H e .…”

“…In [10] [12] we introduced an approach, called the match-mediated difference (MMD), to combining the information from real and synthetic images for static scenes containing real and/or simulated stationary and moving objects. We leveraged Itti and Arbib's [14] concept of the minimal subscene (developed for discourse analysis) to capture how the robot modelled the scene being viewed, how it deployed the simulation and the MMD operation to determine unexpected scene elements and how it requested and assimilated simulation predictions.…”

“…The MMD operation was developed in [10] to effectively fuse information from real and synthetic views of the same scene. The constraint that the real and synthetic scenes have similar appearance was relaxed in [13] so that only the structure needed to be similar.…”

“…In previous work [10][12] [13] we have considered a scenario where an autonomous platform that is searching an area for a target may observe unstable masonry or may need to travel over, by or through unstable rubble. One approach to allow the robot to safely navigate this challenge is to provide a general set of reactive behaviors that produce reasonable behavior under these uncertain and dynamic conditions.…”