Localization from semantic observations via the matrix permanent

Most approaches to robot localization rely on low-level geometric features such as points, lines, and planes. In this paper, we use object recognition to obtain semantic information from the robot’s sensors and consider the task of localizing the robot within a prior map of landmarks, which are annotated with semantic labels. As object recognition algorithms miss detections and produce false alarms, correct data association between the detections and the landmarks on the map is central to the semantic localization problem. Instead of the traditional vector-based representation, we propose a sensor model, which encodes the semantic observations via random finite sets and enables a unified treatment of missed detections, false alarms, and data association. Our second contribution is to reduce the problem of computing the likelihood of a set-valued observation to the problem of computing a matrix permanent. It is this crucial transformation that allows us to solve the semantic localization problem with a polynomial-time approximation to the set-based Bayes filter. Finally, we address the active semantic localization problem, in which the observer’s trajectory is planned in order to improve the accuracy and efficiency of the localization process. The performance of our approach is demonstrated in simulation and in real environments using deformable-part-model-based object detectors. Robust global localization from semantic observations is demonstrated for a mobile robot, for the Project Tango phone, and on the KITTI visual odometry dataset. Comparisons are made with the traditional lidar-based geometric Monte Carlo localization

Conteo de vehículos a partir de vídeos usando machine learning

This work presents a framework for vehicle counting from videos, using deep neural networks as detectors. The framework has 4 stages: preprocessing, detection and classification, tracking, and post-processing. For the detection stage, several deep object detector are compared and 3 new ones are proposed based on Tiny YOLOv3. For the tracking, a new tracker based on IOU is compared against the classic ones: Boosting, KCF, TLD, Mediaflow, MOSSE and CSRT. The comparison is based on 8 multi-object tracking metrics over the Bog19 dataset. The Bog19 dataset is a collection of annotated videos from the city of Bogota. The annotations include bicycles, buses, cars, motorbikes and trucks. Finally, the system is evaluated for the task of vehicle counting on this dataset. For the counting task, the combinations of the proposed detectors with the Medianflow and MOSSE trackers obtain the best results. The founded detectors have the same performance as those of the state of the art but with a higher speed.Este trabajo presenta un framework para el conteo de vehı́culos a partir de videos, utilizando redes neuronales profundas como detectores. El framework tiene 4 etapas: preprocesamiento, detección y clasificación, seguimiento y post-procesamiento. Para la etapa de detección se comparan varios detectores de objetos profundos y se proponen 3 nuevos basados en Tiny YOLOv3. Para el rastreo, se compara un nuevo rastreador basado en IOU con los clásicos: Boosting, KCF, TLD, Mediaflow, MOSSE y CSRT. La comparación se hace en base a 8 métricas de seguimiento multiobjeto sobre el conjunto de datos del Bog19. El conjunto de datos Bog19 es una colección de videos anotados de la ciudad de Bogotá. Las clases de objetos anotados incluyen bicicletas, autobuses, coches, motos y camiones. Finalmente el sistema es evaluado para la tarea de contar vehı́culos en este conjunto de datos. Para la tarea de conteo, las combinaciones de los detectores propuestos y los rastreadores Medianflow y MOSSE obtienen los mejores resultados. Los detectores encontrados tienen el mismo desempeño que los del estado del arte pero con una mayor velocidad.Magíster en Ingeniería - Ingeniería de Sistemas y ComputaciónMaestrí

Low cost underwater acoustic localization

Over the course of the last decade, the cost of marine robotic platforms has significantly decreased. In part this has lowered the barriers to entry of exploring and monitoring larger areas of the earth's oceans. However, these advances have been mostly focused on autonomous surface vehicles (ASVs) or shallow water autonomous underwater vehicles (AUVs). One of the main drivers for high cost in the deep water domain is the challenge of localizing such vehicles using acoustics. A low cost one-way travel time underwater ranging system is proposed to assist in localizing deep water submersibles. The system consists of location aware anchor buoys at the surface and underwater nodes. This paper presents a comparison of methods together with details on the physical implementation to allow its integration into a deep sea micro AUV currently in development. Additional simulation results show error reductions by a factor of three.Comment: 73rd Meeting of the Acoustical Society of Americ