Improving 3D Keypoint Detection from Noisy Data Using Growing Neural Gas

3D sensors provides valuable information for mobile robotic tasks like scene classification or object recognition, but these sensors often produce noisy data that makes impossible applying classical keypoint detection and feature extraction techniques. Therefore, noise removal and downsampling have become essential steps in 3D data processing. In this work, we propose the use of a 3D filtering and down-sampling technique based on a Growing Neural Gas (GNG) network. GNG method is able to deal with outliers presents in the input data. These features allows to represent 3D spaces, obtaining an induced Delaunay Triangulation of the input space. Experiments show how the state-of-the-art keypoint detectors improve their performance using GNG output representation as input data. Descriptors extracted on improved keypoints perform better matching in robotics applications as 3D scene registration.

Dispersivity Determination Through a Modeling Approach From a Tracer Test Based on Total Br Concentration in Soil Samples

Determination of reliable solute transport parameters is an essential aspect for the characterization of the mechanisms and processes involved in solute transport (e.g., pesticides, fertilizers, contaminants) through the unsaturated zone. A rapid inexpensive method to estimate the dispersivity parameter at the field scale is presented herein. It is based on the quantification by the X-ray fluorescence solid-state technique of total bromine in soil, along with an inverse numerical modeling approach. The results show that this methodology is a good alternative to the classic Br− determination in soil water by ion chromatography. A good agreement between the observed and simulated total soil Br is reported. The results highlight the potential applicability of both combined techniques to infer readily solute transport parameters under field conditions.

Biogas from MSW landfill: Composition and determination of chlorine content with the AOX (adsorbable organically bound halogens) technique

An exhaustive characterization of the biogas from some waste disposal facilities has been carried out. The analysis includes the main components (methane, carbon dioxide, nitrogen and oxygen) as well as trace components such as hydrogen sulphide, ammonia and VOCs (volatile organic compounds) including siloxanes and halogenated compounds. VOCs were measured by GC/MS (Gas Chromatography/Mass Spectrometry) using two different procedures: thermal desorption of the Tenax TA and Carbotrap 349 tubes and SPME (Solid Phase Micro-Extraction). A method has been established to measure the total halogen content of the biogas with the AOX (adsorbable organically bound halogens) technique. The equipment used to analyze the samples was a Total Organic Halogen Analyzer (TOX-100). Similar results were obtained when comparing the TOX (Total Organic Halogen) values with those obtained by GC/MS. The halogen content in all the samples was under 22 mg Cl/Nm3 which is below the limit of 150 mg/Nm3 proposed in the Spanish Regulations for any use of the biogas. The low chlorine content in the biogas studied, as well as the low content of other trace compounds, makes it suitable for use as a fuel for electricity generating engines.