Martin Beckerman

Treatment of systematic errors in the processing of wide-angle sonar sensor data for robotic navigation

A methodology has been developed for the treatment of systematic errors that arise in the processing of sparse sensor data. A detailed application of this methodology to the construction, from wide-angle sonar sensor data, of navigation maps for use in autonomous robotic navigation is presented. In the methodology, a four-valued labeling scheme and a simple logic for label combination are introduced. The four labels Conflict, Occupied, Empty, and Unknown are used to mark the cells of the navigation maps. The logic allows for the rapid updating of these maps as new information is acquired. Systematic errors are treated by relabeling conflicting pixel assignments. Most of the new labels are obtained from analyses of the characteristic patterns of conflict that arise during the information processing. The remaining labels are determined by imposing an elementary consistent-labeling condition. >

A Bayes-maximum entropy method for multi-sensor data fusion

The author introduces a Bayes-maximum entropy formalism for multisensor data fusion, and presents an application of this methodology to the fusion of ultrasound and visual sensor data as acquired by a mobile robot. In the approach the principle of maximum entropy was applied to the construction of priors and likelihoods from data. Distances between ultrasound and visual points of interest in a dual representation were used to define Gibbs likelihood distributions. Both one- and two-dimensional likelihoods are presented and cast into a form which makes explicit their dependence on the mean. The Bayesian posterior distributions were used to test a null hypothesis, and maximum entropy maps used for navigation were updated using the resulting information from the dual representation.<<ETX>>

DEMO 89-the initial experiment with the HERMIES-III robot

HERMIES-III is a large mobile robot designed for human-scale experiments. The initial experiment with the robot (DEMO 89) was the cleanup of a simulated chemical spill. To perform the experiment, the robot was required to plan a path through an already known world, navigate along the path (avoiding unexpected obstacles), and locate and remove debris from a target area. A description is given of the software system that was developed to perform the experiment. The software system consisted of 19 processes that operated on a distributed set of heterogeneous computers.<<ETX>>

Segmentation and cooperative fusion of laser radar image data

In segmentation, the goal is to partition a given 2D image into regions corresponding to the meaningful surfaces in the underlying physical scene. Segmentation is frequently a crucial step in analyzing and interpreting image data acquired by a variety of automated systems ranging from indoor robots to orbital satellites. In this paper, we present results of a study of segmentation by means of cooperative fusion of registered range and intensity images acquired using a prototype amplitude-modulated CW laser radar. In our approach, we consider three modalities--depth, reflectance and surface orientation. These modalities are modeled as sets of coupled Markov random fields for pixel and line processes. Bayesian inferencing is used to impose constraints of smoothness on the pixel process and linearity on the line process. The latter constraint is modeled using an Ising Hamiltonian. We solve the constrained optimization problem using a form of simulated annealing termed quenched annealing. The resulting model is illustrated in this paper in the rapid quenched, or iterated conditional mode, limit for several laboratory scenes.

Spatial Reasoning In The Treatment Of Systematic Sensor Errors

In processing ultrasonic and visual sensor data acquired by mobile robots systematic errors can occur. The sonar errors include distortions in size and surface orientation due to the beam resolution, and false echoes. The vision errors include, among others, ambiguities in discriminating depth discontinuities from intensity gradients generated by variations in surface brightness. In this paper we present a methodology for the removal of systematic errors using data fror the sonar sensor domain to guide the processing of information in the vision domain, and vice versa. During the sonar data processing some errors are removed from 2D navigation maps through pattern analyses and consistent-labelling conditions, using spatial reasoning about the sonar beam and object characteristics. Others are removed using visual information. In the vision data processing vertical edge segments are extracted using a Canny-like algorithm, and are labelled. Object edge features are then constructed from the segments using statistical and spatial analyses. A least-squares method is used during the statistical analysis, and sonar range data are used in the spatial analysis.

Restoration and fusion of laser-range camera images

In this work we introduce Markov cross entropic priors in the Bayesian restoration and fusion of laser range images. These cross entropic priors are used to model smoothness of the surfaces and linearity of the discontinuities. The priors are defined over a pair of coupled Markov random fields representing the corresponding pixel and line processes. Gibbsian maximum a posteriori estimates are then found using simulated annealing. Range image data are discussed, and results are presented for synthetic range images.

Design and Implementation of Two Concurrent Multi-Sensor Integration Algorithms for Mobile Robotsl

Two multi-sensor integration algorithms useful in mobile robotics applications are reviewed. A minimal set of utilities are then developed which enable implementation of these algorithms on a distributed memory concurrent computer.

Regeneration following traumatic brain injury: Signals, signposts and scaffolds

In this paper, we present a conceptual model of the steps to be taken in nerve regeneration. Starting with early vision we identify the key developmental steps leading to formation functional circuits. We then examine the two main approaches to nerve regeneration — the first centered on activating intrinsic growth restorative functions to injured axons and the second on stem cell based therapies. Guided by the findings in the visual system we propose the early application of patterned electrical stimulation. This has been shown to activate essential signaling pathways, ensure a balance between excitation and inhibition, establish reliable network topography, and provide a supportive framework for experience-driven, electrical activity during the highly plastic critical period. These steps should enhance the effectiveness during a corresponding critical period for motor system rehabilitation and nerve regeneration.

Role of the microenvironment and feedback signaling loops in stabilizing disease states

In this paper, we identify several of the feedback loops that we posit play key roles in establishing and maintaining states of disease in the human body. These routes are established through signaling between the affected cells and cells in their local microenvironment. Signals sent and received by these cells underlie the pathogenesis of diseases ranging from type 2 diabetes and atherosclerosis to cancers to various neurodegenerative disorders. In the tumor stroma, the focus in this paper, signaling loops are established between cancer cells, macrophages, fibroblasts, and others. In the disease state, these loops and the cell populations that sustain them are hypothesized to counter beneficial actions of drugs and eventually render them ineffective. A major challenge for systems medicine is to understand how the cellular microenvironment and its signaling routes stabilize disease states and how these impediments may be overcome.

Data fusion through simulated annealing of registered range and reflectance images

In this paper we present results of a study of registered range and reflectance images acquired using a prototype amplitude-modulated cw laser radar. Ranging devices such as laser radars represent new technologies which are being applied in aerospace, nuclear and other hazardous environments where remote inspections, 3D identifications, and measurements are required. However, data acquired using devices of this type may contain non-stationary, signal- dependent noise, range-reflectance crosstalk, and low-reflectance range artifacts. Low level fusion algorithms play an essential role in achieving reliable performance by handling the complex noise, systematic errors, and artifacts. The objective of our study is the development of a stochastic fusion algorithm which takes as its input the registered image pair and produces as its output a reliable description of the underlying physical scene in terms of locally smooth surfaces separated by well-defined depth discontinuities. To construct the algorithm we model each image as a set of coupled Markov random fields representing pixel and several orders of line processes. Within this framework we (i) impose local smoothness constraints, introducing a simple linearity property in place of the usual sums over clique potentials; (ii) fuse the range and reflectance images through line process couplings; and (iii) use nonstationary, signal- dependent variances, adaptive thresholding, and a form of Markov natural selection. We show that the resulting algorithm yields reliable results even in worst-case scenarios.