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2021, Autonomous Robots
This work presentsObject Landmarks, a new type of visual feature designed for visual localization over major changes in distance and scale. AnObject Landmarkconsists of a bounding box$${\mathbf {b}}$$bdefining an object, a descriptor$${\mathbf {q}}$$qof that object produced by a Convolutional Neural Network, and a set of classical point features within$${\mathbf {b}}$$b. We evaluateObject Landmarkson visual odometry and place-recognition tasks, and compare them against several modern approaches. We find thatObject Landmarksenable superior localization over major scale changes, reducing error by as much as 18% and increasing robustness to failure by as much as 80% versus the state-of-the-art. They allow localization under scale change factors up to 6, where state-of-the-art approaches break down at factors of 3 or more.
2019 International Conference on Robotics and Automation (ICRA), 2019
2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018
International Journal of Engineering Applied Sciences and Technology, 2021
2018 International Joint Conference on Neural Networks (IJCNN), 2018
The Scale Invariant Feature Transform (SIFT) has become a popular feature extractor for vision-based applications. It has been successfully applied to metric localization and mapping using stereo vision and omnivision. In this paper, we present an approach to Monte-Carlo localization using SIFT features for mobile robots equipped with a single perspective camera. First, we acquire a 2D grid map of the environment that contains the visual features. To come up with a compact environmental model, we appropriately down-sample the number of features in the final map. During localization, we cluster close-by particles and estimate for each cluster the set of potentially visible features in the map using ray-casting. These relevant map features are then compared to the features extracted from the current image. The observation model used to evaluate the individual particles considers the difference between the measured and the expected angle of similar features. In real-world experiments, we demonstrate that our technique is able to accurately track the position of a mobile robot. Moreover, we present experiments illustrating that a robot equipped with a different type of camera can use the same map of SIFT features for localization.
Robotics and Autonomous Systems, 2005
2024
Percorremos uma seleção de escritos de Estudos do Som e áreas adjacentes para: (a) colocar em xeque o pressuposto tenaz de uma escuta universal; (b) apresentar dimensões culturalmente específicas da escuta; (c) explorar a escuta como parte do processo etnográfico; e (d) questionar naturalizações da escuta na Antropologia—e.g., no tratamento dispensado à audição de gravações etnográficas. As sessões, em modo híbrido, consistirão em discussões de textos selecionados, de leitura imprescindível. Exceto a primeira, não haverá aulas expositivas. Enfocaremos temas como etnografia do som, etnografia sensorial, ouvido etnográfico, técnica de escuta, paisagem sonora, espaço sonoro, comunicação sonora não verbal, música popular, tortura pelo som e pela música (em Guantánamo, por exemplo) etc. As avaliações levarão em conta a participação nas discussões bem como exposições orais sobre tópicos determinados a serem definidos no início do semestre em função de interesses individuais e coletivos. É facultativa a elaboração de um trabalho final escrito.
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