This article presents a novel state estimation structure developed for a skid-steered, six-wheeled, ARGO® all-terrain vehicle (ATV). The ARGO ATV is a teleoperated unmanned ground vehicle custom fitted with an inertial measurement unit, wheel encoders and a Geographic Positioning System (GPS). This paper offers the following contributions: (1) a hybrid extended Kalman filter/Kalman filter state estimation technique that estimates the motion, orientation and wheel slips for an ARGO and (2) a virtual GPS point generation algorithm that can be used to adjust position estimates using a GPS sensor. Our field experiments reveal that the proposed estimation structure is able to estimate the position, velocity, orientation and longitudinal slip of the ARGO with a reasonable amount of accuracy. In addition, the proposed estimation structure is well suited for online applications and can also incorporate offline virtual GPS data to further improve the accuracy of the position estimates. The proposed estimation structure is also capable of estimating the longitudinal slip for every wheel of the ARGO.

This paper presents a laser scanner based reverse engineering (RE) system that generates accurate 3D solid models of bottles. The proposed system is designed for ease of use and has an open architecture, making it an ideal solution for bottle conveying system manufacturers. Conveyor system manufactures need a versatile RE system that can create 3D models of bottles to develop conveyor systems for their clients. This paper also presents a simple, robust filtering technique that can effectively remove the noise from the scanned laser data, yet retaining the shape of the scanned bottle. An error analysis is performed to compare our technique against another common data filtering technique, and sample 3D models are generated to contrast the effects of the different filtering approaches.

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