We analyse the issues of calibration, stripe location and measurement consistency in low-cost, triangulation-based range sensors using structured laser light. We adopt a direct calibration technique which does not require modelling any specific sensor component or phenomena, and therefore is not limited in accuracy by the inability to model error sources. We compare five algorithms for determining the location of the stripe in the images with subpixel accuracy. We describe data consistency tests based on two-camera geometry, which make it possible to acquire satisfactory range images of highly reflective surfaces with holes. Finally, we sketch the use of our range sensor within an automatic system for 3-D model acquisition from multiple range images. Experimental results illustrating the various topics accuracy are reported and discussed.

This paper describes IMAGINE, a project investigating feature-based recognition of complex 3-D objects from range data. The objects considered are bounded by surfaces of variable complexity, from planes to sculptured patches, which occur commonly in manufactured mechanical components. We introduce our current prototype, IMAGINE2, a complete range-based 3-D recognition system and illustrate briefly the solutions adopted in its modules, namely data acquisition, segmentation, solid object modelling, and model matching. Finally, we demonstrate the system's performance in recognizing a typical industrial component, using an automatically acquired 3-D model.

Kalman's optimum linear filter has proved to be immensely popular in the field of computer vision. A less often quoted contribution of Kalman's to the control theory literature is that of the concepts of controllability and observability which may be used to analyse the state transition and observation equations and give insights into the filter's viability. This paper aims to highlight the usefulness of these two ideas during the design stage of the filter and, as well as presenting the standard solutions for linear systems, uses a practical vision application (that of tracking plants for an autonomous crop protection vehicle) to illustrate a useful special case where these methods may be applied to a non-linear system. The application of tests for controllability and observability to the practical non-linear system give not only confirmation that the filter will be able to produce stable estimates, but also gives a lower bound on the number of features required from each ...

Kalman’s optimum linear filter has proved to be immensely popular in the field of computer vision. A less often quoted contribution of Kalman’s to the control theory literature is that of the concepts of controllability and observability which may be used to analyse the state transition and obser-vation equations and give insights into the filter’s viability. This paper aims to highlight the usefulness of these two ideas during the design stage of the filter and, as well as presenting the standard solutions for linear systems, uses a practical vision application (that of tracking plants for an autonomous crop protection vehicle) to illustrate a useful special case where these methods may be applied to a non-linear system. The application of tests for con-trollability and observability to the practical non-linear system give not only confirmation that the filter will be able to produce stable estimates, but also gives a lower bound on the number of features required from each image for it to do so. 1

This paper describes IMAGINE, a project investigating feature-based recog-nition of complex 3-D objects from range data. The objects considered are bounded by surfaces of variable complexity, from planes to sculptured patches, which occur commonly in manufactured mechanical components. We intro-duce our current prototype, IMAGINE2, a complete range-based 3-D recogni-tion system and illustrate brie y the solutions adopted in its modules, namely data acquisition, segmentation, solid object modelling, and model matching. Finally, we demonstrate the system's performance in recognizing a typical in-dustrial component, using an automatically acquired 3-D model. 1 Introduction: the IMAGINE Project In this paper we describe IMAGINE, a project investigating feature-based recognition of complex 3-D objects from range data. The IMAGINE project has been the l rouge of the vision research of the Machine Vision Unit for many years. Although recognition has been the primary focus, many related aspects have been investigated within the IMAG-