A robust and fast method for 6DoF motion estimation from generalized 3D data

Nowadays, there is an increasing number of robotic applications that need to act in real three-dimensional (3D) scenarios. In this paper we present a new mobile robotics orientated 3D registration method that improves previous Iterative Closest Points based solutions both in speed and accuracy. As an initial step, we perform a low cost computational method to obtain descriptions for 3D scenes planar surfaces. Then, from these descriptions we apply a force system in order to compute accurately and efficiently a six degrees of freedom egomotion. We describe the basis of our approach and demonstrate its validity with several experiments using different kinds of 3D sensors and different 3D real environments.

Combining visual features and Growing Neural Gas networks for robotic 3D SLAM

The use of 3D data in mobile robotics provides valuable information about the robot’s environment. Traditionally, stereo cameras have been used as a low-cost 3D sensor. However, the lack of precision and texture for some surfaces suggests that the use of other 3D sensors could be more suitable. In this work, we examine the use of two sensors: an infrared SR4000 and a Kinect camera. We use a combination of 3D data obtained by these cameras, along with features obtained from 2D images acquired from these cameras, using a Growing Neural Gas (GNG) network applied to the 3D data. The goal is to obtain a robust egomotion technique. The GNG network is used to reduce the camera error. To calculate the egomotion, we test two methods for 3D registration. One is based on an iterative closest points algorithm, and the other employs random sample consensus. Finally, a simultaneous localization and mapping method is applied to the complete sequence to reduce the global error. The error from each sensor and the mapping results from the proposed method are examined.

Indicators of success in the dietary management of overweight and obesity: weight, body fat loss and quality

Concepts: %WL: Percentage of weight loss; %FL: Percentage of fat loss. Objective: evaluate which unit of measurement for weight loss could determine the success or failure of dietary treatment for overweight and obesity. Method: 4,625 consultations carried out on 616 patients in the southeast of Spain from 2006 to 2012. All of the patients were over 25 years of age and suffered from overweight or obesity. The consultations were carried out every fortnight, using the Mediterranean or low-calorie diet. The patients were divided into four groups according to their %WL and %FL. Results: most of the sample consisted of: women; participants between 25-45 years of age; attended consultations for over a month and a half; obese. 80% of the patients obtained a %FL ≥ 5% (15.5 ± 12.8). The groups with a higher %FL obtained significant differences in weight loss (22.6 vs 11.2%, p = 0.000). The multinomial analysis shows significant differences between the groups with the highest %FL and the lowest %WL and %FL: sex (p = 0.006 vs p = 0.005), BMI (p = 0.010 vs p = 0.003) and attendance (p = 0.000 vs p = 0.000). Conclusion: the patients who lost < 5% of fat had higher initial parameters (percentage of weight and fat); most of the sample lost ≥ 5% of fat. This means that the method of personalised dietary treatment results in a high fat loss; fat is an indicator of the quality loss obtained. Recommendations: use the measurement of fat as a complementary unit of measurement to weight loss; establish a limit of 5% to evaluate such loss; and increase this type of research in any method of weight loss.

A simple method to predict elastic settlements in foundations resting on two soils of differing deformability

This paper shows the analysis results obtained from more than 200 finite element method (FEM) models used to calculate the settlement of a foundation resting on two soils of differing deformability. The analysis considers such different parameters as the foundation geometry, the percentage of each soil in contact with the foundation base and the ratio of the soils’ elastic moduli. From the described analysis, it is concluded that the maximum settlement of the foundation, calculated by assuming that the foundation is completely resting on the most deformable soil, can be correlated with the settlement calculated by FEM models through a correction coefficient named “settlement reduction factor” (α). As a consequence, a novel expression is proposed for calculating the real settlement of a foundation resting on two soils of different deformability with maximum errors lower than 1.57%, as demonstrated by the statistical analysis carried out. A guide for the application of the proposed simple method is also explained in the paper. Finally, the proposed methodology has been validated using settlement data from an instrumented foundation, indicating that this is a simple, reliable and quick method which allows the computation of the maximum elastic settlement of a raft foundation, evaluates its suitability and optimises its selection process.