Layered Interpretation of Street View Images

We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving. Recently, stixels, stix-mantics, and tiered scene labeling methods have been proposed to model street view images. We propose a 4-layer street view model, a compact representation over the recently proposed stix-mantics model. Our layers encode semantic classes like ground, pedestrians, vehicles, buildings, and sky in addition to the depths. The only input to our algorithm is a pair of stereo images. We use a deep neural network to extract the appearance features for semantic classes. We use a simple and an efficient inference algorithm to jointly estimate both semantic classes and layered depth values. Our method outperforms other competing approaches in Daimler urban scene segmentation dataset. Our algorithm is massively parallelizable, allowing a GPU implementation with a processing speed about 9 fps.Comment: The paper will be presented in the 2015 Robotics: Science and Systems Conference (RSS

Stixel Based Scene Understanding for Autonomous Vehicles

We propose a stereo vision based obstacle detection and scene segmentation algorithm appropriate for autonomous vehicles. Our algorithm is based on an innovative extension of the Stixel world, which neglects computing a disparity map. Ground plane and stixel distance estimation is improved by exploiting an online learned color model. Furthermore, the stixel height estimation is leveraged by an innovative joined membership scheme based on color and disparity information. Stixels are then used as an input for the semantic scene segmentation providing scene understanding, which can be further used as a comprehensive middle level representation for high-level object detectors

Hybrid Sampling Bayesian Occupancy Filter

International audienceModeling and monitoring dynamic environments is a complex task but is crucial in the field of intelligent vehicle. A traditional way of addressing these issues is the modeling of moving objects, through Detection And Tracking of Moving Objects (DATMO) methods. An alternative to a classic object model framework is the occupancy grid filtering domain. Instead of segmenting the scene into objects and track them, the environment is represented as a regular grid of occupancy, in which each cell is tracked at a sub-object level. The Bayesian Occupancy Filter is a generic occupancy grid framework which predicts the spread of spatial occupancy by estimating cell velocity distributions. However its velocity model, corresponding to a transition histogram per cell, leads to huge data management which in practice makes it hardly compatible to severe computational and hardware constraints, like in many embedded systems. In this paper, we present a new representation for the BOF, describing the environment through a mix of static and dynamic occupancy. This differentiation enables the use of a model adapted to the considered nature: static occupancy is described in a classic occupancy grid, while dynamic occupancy is modeled by a set of moving particles. Both static and dynamic parts are jointly generated and evaluated, their distribution over the cells being adjusted. This approach leads to a more compact model and to drastically improve the accuracy of the results, in particular in term of velocities. Experimental results show that the number of values required to model the velocities have been reduced from a typical 900 per cell (for a 30x30 neighborhood) to less than 2 per cell in average. The massive data compression allows to plan dedicated embedded devices

Detección del Espacio Libre Conducible

La detección del espacio libre conducible es utilizada en la actualidad tanto para la asistencia a la conducción como para el desarrollo de sistemas de conducción completamente autónomos. Habitualmente, este problema se afronta determinando la profundidad en la imagen mediante sensores (LIDAR) o cámaras estéreo. Este trabajo desarrolla una solución para la estimación del espacio libre conducible mediante el análisis de imágenes generadas con una cámara monocular. Inspirándose en una solución propuesta anteriormente, basada en el uso de técnicas de programación dinámica y la valoración de características en una imagen, este trabajo propone una solución escalable a este problema. Para ello se analiza el uso de características geométricas basadas en contornos y apariencia. Por último se muestran resultados de dicha solución para muestras de imágenes del conjunto KITTI para retos orientados a la conducción autónoma.Drivable detection space is currently used for driving assistance and for the development of fully autonomous driving systems. Typically, this problem is tackled by determining the depth in the image through sensors (LIDAR) or stereo cameras. This paper develops a solution for the estimation of free space drivable by analyzing images generated with a monocular camera. Inspired by a solution previously proposed, based in dynamic programming techniques and assessment of features in an image, this paper proposes a scalable solution to this problem. Algorithm use geometric characteristics like appearance and contours based analyzes. Finally we test the results with KITTI road dataset for autonomous driving.La detecció de l'espai lliure conduïble és utilitzada en l'actualitat tant per a l'assistència a la conducció com per al desenvolupament de sistemes de conducció completament autònoms. Habitualment, aquest problema s'afronta determinant la profunditat en la imatge mitjançant sensors (LIDAR) o càmeres estèreo. Aquest treball desenvolupa una solució per a l'estimació de l'espai lliure conduïble mitjançant l'anàlisi d'imatges generades amb una càmera monocular. Inspirant-se en una solució proposada anteriorment, basada en l'ús de tècniques de programació dinàmica i la valoració de característiques en una imatge, aquest treball proposa una solució escalable a aquest problema. Per a això s'analitza l'ús de característiques geomètriques basades en contorns i aparença. Finalment es mostren resultats d'aquesta solució per a mostres d'imatges del conjunt KITTI per a reptes orientats a la conducció autònoma

An evaluation framework for stereo-based driver assistance

This is the post-print version of the Article - Copyright @ 2012 Springer VerlagThe accuracy of stereo algorithms or optical flow methods is commonly assessed by comparing the results against the Middlebury database. However, equivalent data for automotive or robotics applications rarely exist as they are difficult to obtain. As our main contribution, we introduce an evaluation framework tailored for stereo-based driver assistance able to deliver excellent performance measures while circumventing manual label effort. Within this framework one can combine several ways of ground-truthing, different comparison metrics, and use large image databases. Using our framework we show examples on several types of ground truthing techniques: implicit ground truthing (e.g. sequence recorded without a crash occurred), robotic vehicles with high precision sensors, and to a small extent, manual labeling. To show the effectiveness of our evaluation framework we compare three different stereo algorithms on pixel and object level. In more detail we evaluate an intermediate representation called the Stixel World. Besides evaluating the accuracy of the Stixels, we investigate the completeness (equivalent to the detection rate) of the StixelWorld vs. the number of phantom Stixels. Among many findings, using this framework enables us to reduce the number of phantom Stixels by a factor of three compared to the base parametrization. This base parametrization has already been optimized by test driving vehicles for distances exceeding 10000 km