Localization and Mapping for Self-Driving Vehicles:A Survey

The upsurge of autonomous vehicles in the automobile industry will lead to better driving experiences while also enabling the users to solve challenging navigation problems. Reaching such capabilities will require significant technological attention and the flawless execution of various complex tasks, one of which is ensuring robust localization and mapping. Recent surveys have not provided a meaningful and comprehensive description of the current approaches in this field. Accordingly, this review is intended to provide adequate coverage of the problems affecting autonomous vehicles in this area, by examining the most recent methods for mapping and localization as well as related feature extraction and data security problems. First, a discussion of the contemporary methods of extracting relevant features from equipped sensors and their categorization as semantic, non-semantic, and deep learning methods is presented. We conclude that representativeness, low cost, and accessibility are crucial constraints in the choice of the methods to be adopted for localization and mapping tasks. Second, the survey focuses on methods to build a vehicle’s environment map, considering both the commercial and the academic solutions available. The analysis proposes a difference between two types of environment, known and unknown, and develops solutions in each case. Third, the survey explores different approaches to vehicles’ localization and also classifies them according to their mathematical characteristics and priorities. Each section concludes by presenting the related challenges and some future directions. The article also highlights the security problems likely to be encountered in self-driving vehicles, with an assessment of possible defense mechanisms that could prevent security attacks in vehicles. Finally, the article ends with a debate on the potential impacts of autonomous driving, spanning energy consumption and emission reduction, sound and light pollution, integration into smart cities, infrastructure optimization, and software refinement. This thorough investigation aims to foster a comprehensive understanding of the diverse implications of autonomous driving across various domains

Proposta de um modelo híbrido para alinhamento de nuvens de pontos 3D derivadas do Sistema LiDAR Terrestre no modelo estático

Orientador: Prof. Dr. Daniel Rodrigues dos SantosTese (doutorado) - Universidade Federal do Paraná, Setor de Ciências da Terra, Programa de Pós-Graduação em Ciências Geodésicas. Defesa : Curitiba, 22/02/2019Inclui referências: p. 90-93Resumo: Com o avanço tecnológico, o SLT tem sido cada vez mais empregado em levantamentos topográficos da superfície física da Terra. Esta tese tem como objetivo apresentar uma proposta de um modelo híbrido para alinhamento de nuvens de pontos tridimensionais derivadas do SLT no modo estático. Primeiramente, o método proposto encontra valores aproximados entre pares de nuvens de pontos 3D usando uma combinação do algoritmo SIFT3D e PFH. Em seguida, linhas retas, pontos e planos são empregados para estimar os parâmetros de orientação relativa (rotação e translação) entre os pares de nuvens de pontos. Finalmente, a posição do SLT é refinada usando um ajustamento sequencial de nuvens de pontos baseado em um modelo híbrido e estrutura em grafo. Para avaliar a potencialidade do método proposto foram conduzidos experimentos usando dados derivados do SLT no modo estático de uma área urbana. Os resultados obtidos na etapa de estimativa de valores iniciais dos parâmetros de orientação relativa, propiciaram aproximações suficientemente adequadas para o modelo de refinamento híbrido proposto para determinação dos parâmetros de rotação e de translação para os pares de nuvens de pontos, obtendo uma rápida convergência para um mínimo local. Finalizando foi realizado o refinamento das estações de observação do SLT em um sistema de referência global usando uma abordagem ponto-a-plano (hibrida) e comparado o método proposto com o de Lu e Milios (1997), apresentando melhor acurácia, justificando a relevância desta abordagem. Palavras-chave: Registro de nuvens de pontos 3D; LASER Scanning Terrestre; SIFT3D+PFH; combinação de pontos, linha retas e planos.Abstract: With the technological advance, SLT has been increasingly used in topographic surveys of the physical surface of the Earth. This thesis aims to present a proposal of a hybrid model for the alignment of three-dimensional point clouds derived from the SLT. First, the proposed method identifies approximate values between pairs of 3D point clouds using a combination of the SIFT3D and PFH algorithm. Then, straight lines, points and planes are used to estimate the relative orientation parameters (rotations and translations) between pairs of point clouds. Finally, the position each SLT is refined using a sequential adjustment of point clouds based on a hybrid model and graph structure. To evaluate the potentiality of the proposed method, experiments were conducted using a date set derived from a SLT in the static mode over an urban area. The obtained results in the initial values estimation of the relative orientation parameters provided suitable approximations for the proposed hybrid refinement model to determine the rotations and translations parameters for the point cloud pairs, obtaining a fast convergence for a local minimum. Finally, the SLT position stations were refined in a global reference system using a point-to-plan approach (hybrid) and compared the proposed method with Lu and Milios approach (1997), presenting a better accuracy, which justifies the relevance of the proposed methodology. Keywords: Registration of 3D point cloud, Terrestrial Laser Scanning, SIFT3D+PFH, combination of points, straight lines and planes