Lucas de Paula Veronese

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In automated multi-label text categorization, an automatic categorization system should output a label set, whose size is unknown a priori, for each document under analysis. Many machine learning techniques have been used for building such automatic text categorization systems. In this paper, we examine virtual generalizing random access memory weightless(More)
Virtual Generalizing Random Access Memory Weightless Neural Networks (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training and test. In this paper, we present a new approach for traffic sign recognition based on VG-RAM WNN. We evaluate its performance using the German Traffic Sign Recognition Benchmark(More)
In this work, we present an end-to-end framework for precise large-scale mapping with applications in autonomous driving. In special, the problem of mapping complex environments, with features changing from tree-lined streets to urban areas with dense traffic, is studied. The robotic car is equipped with a odometry sensor, a 3D LiDAR Velodyne HDL-32E, a(More)
Mapping and localization are fundamental problems in autonomous robotics. Autonomous robots need to know where they are in their area of operation to navigate through it and to perform activities of interest. In this paper, we propose an Image-Based Global Localization (VibGL) system that uses Virtual Generalizing Random Access Memory Weightless Neural(More)
The Virtual Generalizing Random Access Memory Weightless Neural Network (VG-RAM WNN) is an effective machine learning technique that offers simple implementation and fast training. One disadvantage of VG-RAM WNN, however, is the test time for applications with many training samples, i.e. large multi-class classification applications. In such cases, the test(More)
We present the Model-Predictive Motion Planner (MPMP) of the Intelligent Autonomous Robotic Automobile (IARA). IARA is a fully autonomous car that uses a path planner to compute a path from its current position to the desired destination. Using this path, the current position, a goal in the path and a map, IARA's MPMP is able to compute smooth trajectories(More)
We present a simple yet effective obstacle avoider for the Intelligent and Autonomous Robotic Automobile (IARA). At each or several motion planning cycles, the IARA's obstacle avoider firstly receives as input an updated map of the environment around the car, the current car's state relative to the map, and a trajectory from the current car's state to the(More)
We tackle the problem of automating the categorization of companies according to their economic activities using business descriptions in free text format as input. This categorization is vital to fundamental aspects of national governmental administration such as short, medium and long term planning and taxation. As the number of categories considered is(More)
We propose a light-weight yet accurate localization system for autonomous cars that operate in large-scale and complex urban environments. It provides appropriate localization accuracy and processing time at high frequency suitable for fast control actions, besides low power consumption desirable for limited energy availability in commercial cars. The(More)
Vehicle localization in large-scale urban environments has been commonly addressed as a map-matching problem in the literature. Generally, the maps are 2D images of the world where each pixel covers a part of it. However, building maps for large-scale urban environments requires driving the vehicle along the desired path at least once. In order to simplify(More)