Novel point-to-point scan matching algorithm based on cross-correlation

The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings. The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.Web of Scienceart. ID 646394

A Bioinspired Neural Model Based Extended Kalman Filter for Robot SLAM

Robot simultaneous localization and mapping (SLAM) problem is a very important and challenging issue in the robotic field. The main tasks of SLAM include how to reduce the localization error and the estimated error of the landmarks and improve the robustness and accuracy of the algorithms. The extended Kalman filter (EKF) based method is one of the most popular methods for SLAM. However, the accuracy of the EKF based SLAM algorithm will be reduced when the noise model is inaccurate. To solve this problem, a novel bioinspired neural model based SLAM approach is proposed in this paper. In the proposed approach, an adaptive EKF based SLAM structure is proposed, and a bioinspired neural model is used to adjust the weights of system noise and observation noise adaptively, which can guarantee the stability of the filter and the accuracy of the SLAM algorithm. The proposed approach can deal with the SLAM problem in various situations, for example, the noise is in abnormal conditions. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available

SLAM visual em ambientes híbridos : estudo de caso com robô pioneer

Trabalho de conclusão de curso (graduação)—Universidade de Brasília, Instituto de Ciências Exatas, Departamento de Ciência da Computação, 2018.O problema de Simultaneous Localization and Mapping (SLAM) consiste em um robô obter um mapa do ambiente em que se encontra ao mesmo tempo em que se localiza neste ambiente. Tal mapa permite que um robô realize diversas atividades por meio de planejamento de rotas e localização. Problemas e aplicações recentes de grande interesse, tais como busca e salvamento em áreas de desastre, exploração de minas abandonadas, carros autônomos e robôs para limpeza doméstica podem ser resolvidos ou estão diretamente ligados ao SLAM. Diante da relevância do problema, este trabalho propõe a realização de SLAM visual no 1o andar do prédio de computação da UnB, CiC/Est, utilizando o robô Pioneer 3-AT e sua câmera monocular. Para isto, o algoritmo ORB SLAM foi escolhido e adaptado para interagir com o sistema de comunicação distribuída desenvolvido localmente entre o computador embarcado do Pioneer e o computador de base, um notebook pessoal. Doze operações pelo prédio foram realizadas e analisadas de forma qualitativa, tendo por base o fechamento de loops, relocalização e distorções na escala do mapa gerado, sendo cada mapa obtido analisado por meio da sobreposição da nuvem de pontos gerada ao ground truth real do prédio. Por fim, as principais dificuldades encontradas são discutidas. Para trabalhos futuros, seria interessante unir um algoritmo de exploração inteligente capaz de auxiliar o vSLAM na geração dos mapas, tendo em vista os problemas explorados e discutidos nas análises. Outras aplicações interessantes seriam utilizar o mapa gerado para permitir que um robô realize a limpeza do prédio ou entregue documentos nas salas dos docentes.The SLAM problem consists of a robot getting a map of the environment in which it is at the same time that it is located in this environment. Such a map allows a robot to perform various activities through route planning and location. Recent issues and applications of big interest, such as search and rescue in disaster areas, exploration of abandoned mines, self-driving cars and domestic cleaning robots can be solved or are directly connected to SLAM. In view of the relevance of the problem, this work proposes the realization of visual SLAM in the first floor of the CiC/ Est building at UnB, , using the Pioneer 3-AT robot and its monocular camera. To do this, the ORB SLAM algorithm was chosen and adapted to interact with the distributed communication system developed locally between the Pioneer embedded computer and a base personal computer. Twelve operations around the building were performed and analyzed in a qualitative way, based on loops, relocalization and distortions in the scale of the generated map, each map being analyzed and compared by overlapping the cloud of points generated in the ground truth building. In the end, the main difficulties encountered are discussed. For future work, it would be interesting to combine an intelligent exploration algorithm capable of supporting vSLAM in map generation, in view of the problems explored and discussed in the analyzes. Other interesting applications would be to use the map generated to allow a robot to clean the building or deliver documents in the professors’ rooms

Computational intelligence approaches to robotics, automation, and control [Volume guest editors]

No abstract available