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Publications

Publications by Filipe Neves Santos

2016

Agricultural Wireless Sensor Mapping for Robot Localization

Authors
Duarte, M; dos Santos, FN; Sousa, A; Morais, R;

Publication
ROBOT 2015: SECOND IBERIAN ROBOTICS CONFERENCE: ADVANCES IN ROBOTICS, VOL 1

Abstract
Crop monitoring and harvesting by ground robots in steep slope vineyards is an intrinsically complex challenge, due to two main reasons: harsh conditions of the terrain and reduced time availability and unstable localization accuracy of the Global Positioning System (GPS). In this paper the use of agricultural wireless sensors as artificial landmarks for robot localization is explored. The Received Signal Strength Indication (RSSI), of Bluetooth (BT) based sensors/technology, has been characterized for distance estimation. Based on this characterization, a mapping procedure based on Histogram Mapping concept was evaluated. The results allow us to conclude that agricultural wireless sensors can be used to support the robot localization procedures in critical moments (GPS blockage) and to create redundant localization information.

2015

Automatic Eye Localization; Multi-block LBP vs. Pyramidal LBP Three-Levels Image Decomposition for Eye Visual Appearance Description

Authors
Benrachou, DE; dos Santos, FN; Boulebtateche, B; Bensaoula, S;

Publication
PATTERN RECOGNITION AND IMAGE ANALYSIS (IBPRIA 2015)

Abstract
This manuscript presents the performance evaluation of our algorithm that precisely finds human eyes in still gray-scale images and describes the state of the founded eye. This algorithm has been evaluated considering two descriptors - Pyramid transform domain (PLBP) and Multi-Block Histogram LBP (BHLBP), which are extended versions of the Local Binary Pattern descriptor (LBP). For the classification stage, two types of supervised learning techniques have also been evaluated, Support Vector Machine (SVM) and Multilayer Perceptron (MLP). The proposed method is assessed on the Face Recognition Grand Challenge (BioID) and (CAS-PEAL-R1) databases, and experimental results demonstrate improved performance than some state-of-the-art eye detection approaches.

2013

Fast 3D Map Matching Localisation Algorithm

Authors
Pinto, M; Moreira, AP; Matos, A; Sobreira, H; Santos, F;

Publication
Journal of Automation and Control Engineering - JOACE

Abstract

2013

Towards Extraction of Topological Maps from 2D and 3D Occupancy Grids

Authors
Santos, FN; Moreira, AP; Costa, PC;

Publication
PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2013

Abstract
Cooperation with humans is a requirement for the next generation of robots so it is necessary to model how robots can sense, know, share and acquire knowledge from human interaction. Instead of traditional SLAM (Simultaneous Localization and Mapping) methods, which do not interpret sensor information other than at the geometric level, these capabilities require an environment map representation similar to the human representation. Topological maps are one option to translate these geometric maps into a more abstract representation of the the world and to make the robot knowledge closer to the human perception. In this paper is presented a novel approach to translate 3D grid map into a topological map. This approach was optimized to obtain similar results to those obtained when the task is performed by a human. Also, a novel feature of this approach is the augmentation of topological map with features such as walls and doors.

2014

A visual place recognition procedure with a Markov chain based filter

Authors
dos Santos, FN; Costa, P; Moreira, AP;

Publication
2014 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC)

Abstract
Recognizing a place with a visual glance is the first capacity used by humans to understand where they are. Making this capacity available to robots will make it possible to increase the redundancy of the localization systems available in the robots, and improve semantic localization systems. However, to achieve this capacity it is necessary to build a robust visual place recognition procedure that could be used by an indoor robot. This paper presents an approach that from a single image estimates the robot location in the semantic space. This approach extracts from each camera image a global descriptor, which is the input of a Support Vector Machine classifier. In order to improve the classifier accuracy a Markov chain formalism was considered to constraint the probability flow according the place connections. This approach was tested using videos acquired from three robots in three different indoor scenarios - with and without the Markov chain filter. The use of Markov chain filter has shown a significantly improvement of the approach accuracy.

2017

Mining the usage patterns of ROS primitives

Authors
Santos, A; Cunha, A; Macedo, N; Arrais, R; dos Santos, FN;

Publication
2017 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2017, Vancouver, BC, Canada, September 24-28, 2017

Abstract

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