Currently, the importance of using unmanned mobile platforms in various industries and production is increasing. This has led to an increase in the need for accurate positioning and safe use of these platforms. The main goal of this research is to develop a vision-based navigation system that is invariant to changes in lighting conditions. For robots that navigate using only cameras, changes in illumination can cause failures during re-localization during autonomous navigation. This article describes a multi-session visual simultaneous localization and mapping (SLAM) approach that allows for the creation of a map consisting of multiple variations of the same location in different lighting conditions. This map can be used any time of day to improve localization both indoors and outdoors, making it suitable for various applications.

The need for accurate positioning and tracking of mobile sensor nodes arises in many applications. To compute position estimates from raw measurements in positioning systems, Bayesian filtering techniques such as Kalman, histogram, or particle filters are frequently employed. A major disadvantage of these techniques is the fact that they compute only a single position estimate in every timestep and, therefore, do not utilize the available information to the full extent. The work presented in this article improves over this current state of the art by instead estimating full node trajectories and, therefore, the most plausible sequence of positions in every timestep. We present two distinct algorithms-for a discrete and for a continuous state-space-and highlight the particular advantages of each variant. Achievable results are shown by simulation and measurement to be more accurate than the traditional Bayesian filtering approach. The most prominent advantage of much more accurate total track length estimation is particularly emphasized.

A novel calibration method is presented for a multi-sensor fusion system in large-scale metrology, which improves the calibration efficiency and reliability. The attitude sensor is composed of a pinhole prism, a converging lens, an area-array camera and a biaxial inclinometer. A mathematical model is established to determine its 3D attitude relative to a cooperative total station by using two vector observations from the imaging system and the inclinometer. There are two areas of unknown parameters in the measurement model that should be calibrated: the intrinsic parameters of the imaging model, and the transformation matrix between the camera and the inclinometer. An integrated calibration method using a three-axis rotary table and a total station is proposed. A single mounting position of the attitude sensor on the rotary table is sufficient to solve for all parameters of the measurement model. A correction technique for the reference laser beam of the total station is also presented to remove the need for accurate positioning of the sensor on the rotary table. Experimental verification has proved the practicality and accuracy of this calibration method. Results show that the mean deviations of attitude angles using the proposed method are less than 0.01°.

Disorders of the spine are one of the oldest known human maladies, with descriptions found in ancient medical literature. As in many other areas of medicine, treatment of patients with these disorders has undergone rapid progress over the last century. Among treatment modalities, spinal instrumentation has been a significant technical advancement. Increased use of spinal instrumentation has spurred the need for accurate positioning of screws/instrumentation and enhanced stability of the construct. Traditionally, these have been placed using anatomic landmarks and intraoperative fluoroscopy. In patients with advanced degenerative disease or trauma, or in those with severe curvature deformity, anatomy can be of limited help. With intraoperative fluoroscopy, limited visualization can be a significant issue, especially in patients with a large body habitus. Image guidance, by providing live, accurate information for placement of instrumentation, significantly improves upon these issues. Image guidance has been used in neurologic surgery during recent decades. It was employed initially for intracranial applications, including treatment of brain disorders, with significant benefits and is now used routinely as an important adjunct tool in the operating room for intracranial neurologic surgery. Only more recently has image guidance been used in spine surgery.

The accurate location of a vehicle in the road is one of the most important challenges in the automotive field. The need for accurate positioning affects several in-vehicle systems like navigators, lane departure warning systems, collision warning and other related sectors such as digital cartography suppliers. The aim of this paper is to evaluate high precision positioning systems that are able to supply an on-the-centimetre accuracy source to develop on-the-lane positioning systems and to be used in future applications as an information source for autonomous vehicles that circulate at high speeds on public roads. In this paper we have performed some on-road experiments, testing several GPS-based systems: Autonomous GPS; RTK Differential GPS with a proprietary GPS base station; RTK Differential GPS connected to the public GPS base station network of the National Geographic Institute of Spain via vehicle-to-infrastructure GPRS communications; and GPS combination with inertial measurement systems (INS) for position accuracy maintenance in degraded satellite signal reception areas. In these tests we show the validity and the comparison of these positioning systems, allowing us to navigate, in some cases, on public roads at speeds near 120 km/h and up to 100 km from the start position without any significant accuracy reduction.

The need for accurate positioning has gained significant interest recently, especially in cluttered environments where signals from satellite navigation systems are not reliable. Positioning systems based on ultrawide bandwidth (UWB) technology have been considered for these environments because UWB signals are able to resolve multipath and penetrate obstacles. These systems usually obtain range measurements from time-of-arrival (TOA) estimation of the first path, which can be a challenge in dense multipath environments. In this paper, we analyze and compare the performance of matched filter (MF) and energy detector (ED) TOA estimators based on thresholding in UWB dense multipath channels. The main advantage of threshold-based estimators is that they have the potential for complete analog implementation and hence they are particularly attractive for applications that require low cost battery-powered devices. Closed-form expressions for the estimator bias and mean square error (MSE) are derived as a function of the signal- to-noise ratio. A comparison with results obtained from Monte Carlo simulation confirms the validity of our analytical approach. This analysis enables us to determine the threshold value that minimizes the MSE, a critical parameter for optimal estimator design. A simple criteria to determine the threshold value is also presented. It is shown that the estimation accuracy is mainly affected by the ambiguity in the selection of the correct peak at the output of the MF or ED, caused by the fading characteristics of the first path. We also evaluate the performance loss of ED estimators with respect to MF estimators. Finally, results based on experimental measurements in an indoor residential environment are presented in order to compare the performance of TOA estimators in realistic environments.

Immobilizing and positioning patients for radiotherapy