João Carlos Virgolino Soares

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• Doctor of Engineering
• Postdoc at Istituto Italiano di Tecnologia

This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in [1]. It integrates data from a range of onboard sensors, including IMUs, encoders,...

Point cloud registration is a critical problem in computer vision and robotics, especially in the field of navigation. Current methods often fail when faced with high outlier rates or take a long time to converge to a suitable solution. In this work, we introduce a novel algorithm for point cloud registration called SANDRO (Splitting strategy for p...

This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in [1]. It integrates data from a range of onboard sensors, including IMUs, encoders,...

Accurate state estimation is crucial for legged robot locomotion, as it provides the necessary information to allow control and navigation. However, it is also challenging, especially in scenarios with uneven and slippery terrain. This paper presents a new Invariant Extended Kalman filter for legged robot state estimation using only proprioceptive...

Grapevine winter pruning is a labor-intensive and repetitive process that significantly influences the quality and quantity of the grape harvest and produced wine of the following season. It requires a careful and expert detection of the point to be cut. Because of its complexity, repetitive nature and time constraint, the task requires skilled lab...

The real-world deployment of fully autonomous mobile robots depends on a robust simultaneous localization and mapping (SLAM) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing environments, where objects are moved or replaced after the robot has already mapped the scene. This paper propose...

A maioria dos sistemas de SLAM visual não é robusta em cenários dinâmicos. Aqueles que lidam com conteúdo dinâmico nas cenas geralmente dependem de métodos baseados em aprendizado profundo para detectar e filtrar objetos dinâmicos. No entanto, esses métodos não conseguem lidar com objetos desconhecidos. Este trabalho apresenta o Panoptic-SLAM, um s...

Localização e Mapeamento Simultâneos é um problema fundamental em robótica móvel. No entanto, a maioria dos algoritmos de SLAM Visual assume um cenário estático, limitando sua aplicabilidade em ambientes do mundo real. Lidar com conteúdo dinâmico em SLAM visual ainda é um problema em aberto. Este trabalho apresenta o primeiro método de SLAM visual...

The real-world deployment of fully autonomous mobile robots depends on a robust SLAM (Simultaneous Localization and Mapping) system, capable of handling dynamic environments, where objects are moving in front of the robot, and changing environments, where objects are moved or replaced after the robot has already mapped the scene. This paper present...

People following is an important task in mobile robotics, with applications in several areas, such as industry, hospitals and home services. Recent advances on deep learning techniques and the availability of RGB-D sensors resulted in a higher robustness in identifying the target during these tasks. This paper presents a system for people following...

Simultaneous Localization and Mapping is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario, limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on purely geometric approaches. Dee...

Wall-painting is an important task in construction that has an increasing necessity for automation. This paper presents a navigation system for a wall-painting robot based on map corners. Using an occupancy grid map as input of the system, the proposed method is able to generate a path following the walls of the environment. The main advantage of t...

Simultaneous Localization and Mapping (SLAM) is a fundamental problem in mobile robotics. However, the majority of Visual SLAM algorithms assume a static scenario , limiting their applicability in real-world environments. Dealing with dynamic content in Visual SLAM is still an open problem, with solutions usually relying on direct or feature-based...

Simultaneous Localization and Mapping (SLAM) is one of the key problems in mobile robotics. However, most of the state-of-the-art SLAM systems only work properly in static environments, limiting their applicability. Performing SLAM in scenarios with dynamic objects is still an open problem, with solutions usually relying on Optical Flow or feature-...

This paper presents the project and development of a Mecanum-wheeled robot for autonomous navigation tasks. The robot is equipped with odometry and a laser sensor for range scans. A Monte Carlo Localization algorithm is used to estimate the pose of the robot in a global coordinate system and, simultaneously, a Grid Map is generated with the range s...

The SLAM problem is currently one of the most important topics in mobile robotics, due to the high number of applications that need its solution. This work proposes a methodology to perform SLAM in indoor environments with RGB-D data. The robot motion is estimated using FOVIS, a robust visual odometry system, and a graph-based probabilistic approac...

Several problems in mobile robotics need probabilistic formulations to handle the inherent uncertainty of motion and sensor measurements. Graph-SLAM is a probabilistic approach to the simultaneous localization and mapping problem that is based on maximum likelihood estimation and non-linear least squares optimization. It consists in generating a gr...

This paper presents the development and implementation of a 3D-SLAM System for indoor applications in a commercial differential drive mobile robot equipped with a RGB-D time-of-flight sensor. A visual odometry approach is used to estimate the pose of the robot in a global coordinate system and, simultaneously, a Point Cloud Map is generated with th...