Le traitement de nuages de points 3D de lidars permet aux robots mobiles autonomes terrestres de construire des modèles sémantiques de l'environnement extérieur dans lequel ils évoluent. Ces modèles sont intéressants car ils représentent des informations qualitatives, et ainsi donnent à un robot la capacité de raisonner à un niveau plus élevé d'abstraction. Le coeur d'un système de modélisation sémantique est la capacité de classifier les observations venant du capteur. Nous proposons un système de classification centré sur l'apprentissage non-supervisé. La prémière couche, la couche intermédiaire, consiste en un modèle de mélange gaussien. Ce modèle est déterminé de manière non-supervisée lors d'une étape de training. Il definit un ensemble de classes intermédiaires qui correspond à une partition fine des classes présentes dans l'environnement. La deuxième couche, la couche finale, consiste en un regroupement des classes intermédiaires dans un ensemble de classes finales qui, elles, sont interprétables dans le contexte de la tâche ciblée. Le regroupement est déterminé par un expert lors de l'étape de training, de manière supervisée, mais guidée par les classes intermédiaires. L'évaluation est basée sur deux jeux de données acquis avec de différents lidars et possédant différentes caractéristiques. L'évaluation est quantitative pour l'un des jeux de données, et qualitative pour l'autre. La concéption du système utilise la procédure standard de l'apprentissage, basée sur les étapes de training, validation et test. L'opération suit la pipeline standard de classification. Le système est simple, et ne requiert aucun pré-traitement ou post-traitement. / The processing of 3D lidar point clouds enable terrestrial autonomous mobile robots to build semantic models of the outdoor environments in which they operate. Such models are interesting because they encode qualitative information, and thus provide to a robot the ability to reason at a higher level of abstraction. At the core of a semantic modelling system, lies the capacity to classify the sensor observations. We propose a two-layer classi- fication model which strongly relies on unsupervised learning. The first, intermediary layer consists of a Gaussian mixture model. This model is determined in a training step in an unsupervised manner, and defines a set of intermediary classes which is a fine-partitioned representation of the environment. The second, final layer consists of a grouping of the intermediary classes into final classes that are interpretable in a considered target task. This grouping is determined by an expert during the training step, in a process which is supervised, yet guided by the intermediary classes. The evaluation is done for two datasets acquired with different lidars and possessing different characteristics. It is done quantitatively using one of the datasets, and qualitatively using another. The system is designed following the standard learning procedure, based on a training, a validation and a test steps. The operation follows a standard classification pipeline. The system is simple, with no requirement of pre-processing or post-processing stages.

Successful and high precision localization is an important feature for autonomous vehicles in an urban environment. GPS solutions are not good on their own and laser, sonar and radar are often used as complementary sensors. Localization with these sensors requires the use of techniques grouped under the acronym SLAM (Simultaneous Localization And Mapping). These techniques work by comparing the current sensor inputs to either an incrementally built or known map, also adding the information to the map.Most of the SLAM techniques assume the environment to be static, which means that dynamics and clutter in the environment might cause SLAM to fail. To ob-tain a more robust algorithm, the dynamics need to be dealt with. This study seeks a solution where measurements from different points in time can be used in pairwise comparisons to detect non-static content in the mapped area. Parked cars could for example be detected at a parking lot by using measurements from several different days.The method successfully detects most non-static objects in the different test datasets from the sensor. The algorithm can be used in conjunction with Pose-SLAM to get a better localization estimate and a map for later use. This map is good for localization with SLAM or other techniques since only static objects are left in it.

Indiana University-Purdue University Indianapolis (IUPUI) / Analyzing behaviors of objects on the road is a complex task that requires data from various sensors and their fusion to recreate the movement of objects with a high degree of accuracy. A data collection and processing system are thus needed to track the objects accurately in order to make an accurate and clear map of the trajectories of objects relative to various coordinate frame(s) of interest in the map. Detection and tracking moving objects (DATMO) and Simultaneous localization and mapping (SLAM) are the tasks that needs to be achieved in conjunction to create a clear map of the road comprising of the moving and static objects. These computational problems are commonly solved and used to aid scenario reconstruction for the objects of interest. The tracking of objects can be done in various ways, utilizing sensors such as monocular or stereo cameras, Light Detection and Ranging (LIDAR) sensors as well as Inertial Navigation systems (INS) systems. One relatively common method for solving DATMO and SLAM involves utilizing a 3D LIDAR with multiple monocular cameras in conjunction with an inertial measurement unit (IMU) allows for redundancies to maintain object classification and tracking with the help of sensor fusion in cases when sensor specific traditional algorithms prove to be ineffectual when either sensor falls short due to their limitations. The usage of the IMU and sensor fusion methods relatively eliminates the need for having an expensive INS rig. Fusion of these sensors allows for more effectual tracking to utilize the maximum potential of each sensor while allowing for methods to increase perceptional accuracy. The focus of this thesis will be the dock-less e-scooter and the primary goal will be to track its movements effectively and accurately with respect to cars on the road and the world. Since it is relatively more common to observe a car on the road than e-scooters, we propose a data collection system that can be built on top of an e-scooter and an offline processing pipeline that can be used to collect data in order to understand the behaviors of the e-scooters themselves. In this thesis, we plan to explore a data collection system involving a 3D LIDAR sensor and multiple monocular cameras and an IMU on an e-scooter as well as an offline method for processing the data to generate data to aid scenario reconstruction.

Este projeto de mestrado visa a pesquisa e o desenvolvimento de métodos e algoritmos, relacionados ao uso de radares, visão computacional, calibração e fusão de sensores em veículos autônomos/inteligentes para fazer a detecção de obstáculos. O processo de detecção de obstáculos se divide em três etapas, a primeira é a leitura de sinais de Radar, do LiDAR e a captura de dados da câmera estéreo devidamente calibrados, a segunda etapa é a fusão de dados obtidos na etapa anterior (Radar+câmera, Radar+LIDAR 3D), a terceira etapa é a extração de características das informações obtidas, identificando e diferenciando o plano de suporte (chão) dos obstáculos, e finalmente realizando a detecção dos obstáculos resultantes da fusão dos dados. Assim é possível diferenciar os diversos tipos de elementos identificados pelo Radar e que são confirmados e unidos aos dados obtidos por visão computacional ou LIDAR (nuvens de pontos), obtendo uma descrição mais precisa do contorno, formato, tamanho e posicionamento destes. Na tarefa de detecção é importante localizar e segmentar os obstáculos para posteriormente tomar decisões referentes ao controle do veículo autônomo/inteligente. É importante destacar que o Radar opera em condições adversas (pouca ou nenhuma iluminação, com poeira ou neblina), porém permite obter apenas pontos isolados representando os obstáculos (esparsos). Por outro lado, a câmera estéreo e o LIDAR 3D permitem definir os contornos dos objetos representando mais adequadamente seu volume, porém no caso da câmera esta é mais suscetível a variações na iluminação e a condições restritas ambientais e de visibilidade (p.ex. poeira, neblina, chuva). Também devemos destacar que antes do processo de fusão é importante alinhar espacialmente os dados dos sensores, isto e calibrar adequadamente os sensores para poder transladar dados fornecidos por um sensor referenciado no próprio sistema de coordenadas para um outro sistema de coordenadas de outro sensor ou para um sistema de coordenadas global. Este projeto foi desenvolvido usando a plataforma CaRINA II desenvolvida junto ao Laboratório LRM do ICMC/USP São Carlos. Por fim, o projeto foi implementado usando o ambiente ROS, OpenCV e PCL, permitindo a realização de experimentos com dados reais de Radar, LIDAR e câmera estéreo, bem como realizando uma avaliação da qualidade da fusão dos dados e detecção de obstáculos comestes sensores. / This masters project aims to research and develop methods and algorithms related to the use of radars, computer vision, calibration and sensor data fusion in autonomous / intelligent vehicles to detect obstacles. The obstacle detection process is divided into three stages, the first one is the reading of Radar, LiDAR signals and the data capture of the stereo camera properly calibrated, the second stage is the fusion of data obtained in the previous stage(Radar + Camera, Radar + 3D LIDAR), the third step is the extraction of characteristics of the information obtained, identifying and differentiating the support plane(ground) of the obstacles, and finally realizing the detection of the obstacles resulting from the fusion of the data. Thus it is possible to differentiate types of elements identified by the Radar and that are confirmed and united to the data obtained by computational vision or LIDAR (point cloud), obtaining amore precise description of the contour, format, size and positioning of these. During the detection task it is important to locate and segment the obstacles to later make decisions regarding the control of the autonomous / intelligent vehicle. It is important to note that Radar operates in adverse conditions (little or no light, with dust or fog), but allows only isolated points representing obstacles (sparse), where on the other hand, the stereo camera and LIDAR 3D allow to define the shapeand size of objects. As for the camera, this is more susceptible to variations in lighting and to environmental and visibility restricted conditions (eg dust, haze, rain). It is important to spatially align the sensor data, calibrating the sensors appropriately, to be able to translate data provided by a sensor referenced in the coordinate system itself to another coordinate system of another sensor or to a global coordinate system. This project was developed using the CaRINA II platform developed by the LRM Laboratory ICMC / USP São Carlos. Finally, the project was implemented using the ROS, OpenCV and PCL environments, allowing experiments with real data from Radar, LIDAR and stereo camera, as well as performing an evaluation of the quality of the data fusion and detection of obstacles with these sensors .

Este projeto de mestrado visa a pesquisa e o desenvolvimento de métodos e algoritmos, relacionados ao uso de radares, visão computacional, calibração e fusão de sensores em veículos autônomos/inteligentes para fazer a detecção de obstáculos. O processo de detecção de obstáculos se divide em três etapas, a primeira é a leitura de sinais de Radar, do LiDAR e a captura de dados da câmera estéreo devidamente calibrados, a segunda etapa é a fusão de dados obtidos na etapa anterior (Radar+câmera, Radar+LIDAR 3D), a terceira etapa é a extração de características das informações obtidas, identificando e diferenciando o plano de suporte (chão) dos obstáculos, e finalmente realizando a detecção dos obstáculos resultantes da fusão dos dados. Assim é possível diferenciar os diversos tipos de elementos identificados pelo Radar e que são confirmados e unidos aos dados obtidos por visão computacional ou LIDAR (nuvens de pontos), obtendo uma descrição mais precisa do contorno, formato, tamanho e posicionamento destes. Na tarefa de detecção é importante localizar e segmentar os obstáculos para posteriormente tomar decisões referentes ao controle do veículo autônomo/inteligente. É importante destacar que o Radar opera em condições adversas (pouca ou nenhuma iluminação, com poeira ou neblina), porém permite obter apenas pontos isolados representando os obstáculos (esparsos). Por outro lado, a câmera estéreo e o LIDAR 3D permitem definir os contornos dos objetos representando mais adequadamente seu volume, porém no caso da câmera esta é mais suscetível a variações na iluminação e a condições restritas ambientais e de visibilidade (p.ex. poeira, neblina, chuva). Também devemos destacar que antes do processo de fusão é importante alinhar espacialmente os dados dos sensores, isto e calibrar adequadamente os sensores para poder transladar dados fornecidos por um sensor referenciado no próprio sistema de coordenadas para um outro sistema de coordenadas de outro sensor ou para um sistema de coordenadas global. Este projeto foi desenvolvido usando a plataforma CaRINA II desenvolvida junto ao Laboratório LRM do ICMC/USP São Carlos. Por fim, o projeto foi implementado usando o ambiente ROS, OpenCV e PCL, permitindo a realização de experimentos com dados reais de Radar, LIDAR e câmera estéreo, bem como realizando uma avaliação da qualidade da fusão dos dados e detecção de obstáculos comestes sensores. / This masters project aims to research and develop methods and algorithms related to the use of radars, computer vision, calibration and sensor data fusion in autonomous / intelligent vehicles to detect obstacles. The obstacle detection process is divided into three stages, the first one is the reading of Radar, LiDAR signals and the data capture of the stereo camera properly calibrated, the second stage is the fusion of data obtained in the previous stage(Radar + Camera, Radar + 3D LIDAR), the third step is the extraction of characteristics of the information obtained, identifying and differentiating the support plane(ground) of the obstacles, and finally realizing the detection of the obstacles resulting from the fusion of the data. Thus it is possible to differentiate types of elements identified by the Radar and that are confirmed and united to the data obtained by computational vision or LIDAR (point cloud), obtaining amore precise description of the contour, format, size and positioning of these. During the detection task it is important to locate and segment the obstacles to later make decisions regarding the control of the autonomous / intelligent vehicle. It is important to note that Radar operates in adverse conditions (little or no light, with dust or fog), but allows only isolated points representing obstacles (sparse), where on the other hand, the stereo camera and LIDAR 3D allow to define the shapeand size of objects. As for the camera, this is more susceptible to variations in lighting and to environmental and visibility restricted conditions (eg dust, haze, rain). It is important to spatially align the sensor data, calibrating the sensors appropriately, to be able to translate data provided by a sensor referenced in the coordinate system itself to another coordinate system of another sensor or to a global coordinate system. This project was developed using the CaRINA II platform developed by the LRM Laboratory ICMC / USP São Carlos. Finally, the project was implemented using the ROS, OpenCV and PCL environments, allowing experiments with real data from Radar, LIDAR and stereo camera, as well as performing an evaluation of the quality of the data fusion and detection of obstacles with these sensors .

3D LiDAR people detection and tracking applications rely on extracting individual people from the point cloud for reliable tracking. A recurring problem for these applications is under-segmentation caused by people standing close or interacting with each other, which in turn causes the system to lose tracking. To address this challenge, we propose Kernel Density Estimation Clustering with Grid (KDEG) based on Kernel Density Estimation Clustering. KDEG leverages a grid to save density estimates computed in parallel, finding cluster centers by selecting local density maxima in the grid. KDEG reaches a remarkable accuracy of 98.4%, compared to HDBSCAN and Scan Line Run (SLR) with 80.1% and 62.0% accuracy respectively. Furthermore, KDEG is measured to be highly efficient, with a running time similar to state-of-the-art methods SLR and Curved Voxel Clustering. To show the potential of KDEG, an experiment with a real tracking application on two people walking shoulder to shoulder was performed. This experiment saw a significant increase in the number of accurately tracked frames from 5% to 78% by utilizing KDEG, displaying great potential for real-world applications.  In parallel, we also explored HDBSCAN as an alternative to DBSCAN. We propose a number of modifications to HDBSCAN, including the projection of points to the groundplane, for improved clustering on human groups. HDBSCAN with the proposed modifications demonstrates a commendable accuracy of 80.1%, surpassing DBSCAN while maintaining a similar running time. Running time is however found to be lacking for both HDBSCAN and DBSCAN compared to more efficient methods like KDEG and SLR. / <p>Arbetet är gjort på plats i Tokyo på Chuo Universitet utan samverkan från Umeå Universitet såsom utbytesprogram eller liknande.</p><p>Arbetet är delvis finansierat av Scandinavia-Japan Sasakawa Foundation.</p><p>Arbetet gick inte under vanlig termin, utan började 2023/05/01 och slutade 2023/08</p>