Peter Whaite

Autonomous exploration: driven by uncertainty

Passively accepting measurements of the world is not enough, as the data we obtain is always incomplete, and the inferences made from it uncertain to a degree which is often unacceptable. If we are to build machines that operate autonomously, they will always be faced with this dilemma, and can only be successful if they play a much more active role. This paper presents such a machine. It deliberately seeks out those parts of the world which maximize the fidelity of its internal representations, and keeps searching until those representations are acceptable. We call this paradigm autonomous exploration, and the machine an autonomous explorer. This paper has two major contributions. The first is a theory that tells us how to explore, and which confirms the intuitive ideas we have put forward previously. The second is an implementation of that theory. In our laboratory, we have constructed a working autonomous explorer and here, for the first time, show it in action. The system is entirely bottom-up and does not depend on any a priori knowledge of the environment. To our knowledge, it is the first to have successfully closed the loop between gaze planning and the inference of complex 3D models.

Darboux frames, snakes, and super-quadrics: geometry from the bottom up

A representational and a computational model for deriving 3-D articulated volumetric descriptions of objects from laser rangefinder data is described. This method is purely bottom up: it relies on general assumptions cast in terms of differential geometry. Darboux frames, snakes, and superquadrics form the basis of this representation, and curvature consistency provides the computational framework. The organization is hierarchical. Darboux frames are used to describe the local surface, whereas snakes are used to interpolate between features, particularly those that serve to partition a surface into its constituent parts. Superquadrics are used to characterize the 3-D shape of each surface partition. The result is a set of connected volumetric primitives that serve to describe the overall shape of an object. Examples that show how the approach performs on data acquired with a laser rangefinder are included. >

Recovery of Volumetric Object Descriptions From Laser Rangefinder Images

This paper describes a representation and computational model for deriving three dimensional, articulated volumetric descriptions of objects from laser rangefinder data. What differentiates this work from other approaches is that it is purely bottom-up, relying on general assumptions cast in terms of differential geometry.

From uncertainty to visual exploration

The question posed is what can be inferred from ambiguity in processes of visual interpretation? Much emphasis is naturally placed on the form of constraints used to minimize ambiguity, particularly as they pertain to such issues as perceptual acceptability. The authors feel that it is perhaps more instructive to consider what can be learned from situations where different interpretations of data are possible, i.e., the ambiguity of perception. This immediately raises a number of issues regarding the characterization of ambiguity, communicating it to other visual processes, and using ambiguity to further refine visual interpretation. The context in which the authors discuss these problems is the interpretation of scene geometry in the form of volumetric models. They describe a representation for ambiguity in terms of an ellipsoid of confidence in which there is a finite probability that the true parameters of the model can be found.<<ETX>>

Darboux frames, snakes, and super-quadrics: geometry from the bottom-up

A representation and a computational model for deriving three-dimensional articulated volumetric descriptions of objects from laser range-finder data are described. What differentiates this work from other approaches is that it is purely bottom up, relying on general assumptions cast in terms of differential geometry. Darboux frames, snakes, and superquadrics form the basis of this representation, and curvature consistency provides the computational framework. The organization is hierarchical. Darboux frames are used to describe the local surface, while snakes are used to interpolate between features, particularly those that serve to partition a surface into its constituent parts. Superquadrics are subsequently used to characterize the 3-D shape of each surface partition. The result is a set of connected volumetric primitives which serve to describe the overall shape of an object. A set of examples showing how the approach performs on data acquired with a laser range finder is included.<<ETX>>

Active exploration: Knowing when we're wrong

Many strategies in computer vision assume the existence of general purpose models that can be used to characterize a scene or environment at various levels of abstraction. The usual assumptions are that a selected model is competent to describe a particular attribute and that the parameters of this model can be estimated by interpreting the input data in an appropriate manner. The authors consider the problem of determining when these assumptions break down so that an alternate model may be considered or further interpretation of data performed. Specifically, how this can be accomplished is analyzed within the framework of an approach that actively builds a description of the environment from several different viewpoints. It is shown that a gaze planning strategy used to minimize model parameter variance can also be used to decide whether the model itself provides an adequate description of the environment.<<ETX>>

On the sequential determination of model misfit

Many strategies in computer vision assume the existence of general purpose models that can be used to characterize a scene or environment at various levels of abstraction. The usual assumptions are that a selected model is competent to describe a particular attribute and that the parameters of this model can be estimated by interpreting the input data in an appropriate manner (e.g., location of lines and edges, segmentation into parts or regions, etc.). This paper considers the problem of how to determine when those assumptions break down. The traditional approach is to use statistical misfit measures based on an assumed sensor noise model. The problem is that correct operation often depends critically on the correctness of the noise model. Instead, we show how this can be accomplished with a minimum of a priori knowledge and within the framework of an active approach which builds a description of environment structure and noise over several viewpoints.

Parametric shape recognition using a probabilistic inverse theory

Abstract This paper describes a new framework for parametric shape recognition based on a probabilistic model of inverse theory first introduced by Tarantola. The key result is a method for generating classifiers in the form of conditional probability densities for recognizing an unknown from a set of reference models. Our procedure is automatic. Off-line, it invokes an autonomous process to estimate reference model parameters and their statistics. On-line, during measurement, it combines these with a priori context-dependent information, as well as the parameters and statistics estimated for an unknown object, into a single description. That description, a conditional probability density function, represents the likelihood of correspondence between the unknown and a particular reference model. The paper also describes the implementation of this procedure in a system for automatically generating and recognizing 3-D part-oriented models. Specifically we show that recognition performance is near perfect for cases in which complete surface information is accessible to the algorithm, and that it falls off gracefully (minimal false-positive response) when only partial information is available. This leads to the possibility of an active recognition strategy in which the belief measures associated with each classification can be used as feedback for the acquisition of further evidence as required.

Model building and autonomous exploration

We define autonomous exploration as a process by which an active observer can interact with its surroundings, e.g., by intentionally moving around and collecting information, in order to learn about its environment. Such an ability is essential for autonomous systems that must operate in unstructured environments, i.e., where it is difficult (if not impossible) to characterize the environment beforehand. This paper describes a working system that implements an autonomous explorer whose function is to describe the environment in terms of articulated volumetric models. A novel feature of the system is that it uses feedback to reduce reliance on a priori knowledge.