Virtual 3D City Model for Navigation in Urban Areas

Cappelle, Cindy; Najjar, Maan E. El; Charpillet, François; Pomorski, Denis

doi:10.1007/s10846-011-9594-0

Cited by 39 publications

(17 citation statements)

References 37 publications

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“…In addition, thanks to the prior knowledge, stationary objects such as infrastructure are distinguished from stopped objects on the road. This is a behavior similar to that of the Capelle et al system that uses 3D city models [24].…”

Section: B Results Of Obstacle Detectionsupporting

confidence: 61%

Map-Aided Evidential Grids for Driving Scene Understanding

Kurdej

Moras²,

Cherfaoui

et al. 2015

IEEE Intell. Transport. Syst. Mag.

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Evidential grids have recently been shown to have interesting properties for mobile object perception. Possessing only partial information is a frequent situation when driving in complex urban areas, and by making use of the Dempster-Shafer framework, evidential grids are able to handle partial information efficiently. This article deals with a lidar perception scheme that is enhanced by geo-referenced maps used as an additional source of information in a multigrid fusion framework. The paper looks at the key stages of such a data fusion process and presents an adaptation of the conjunctive combination rule for refining the analysis of conflicting information. This method relies on temporal accumulation to distinguish between stationary and moving objects, and applies contextual discounting for modeling information obsolescence. As a result, the method is able to better characterize the state of the occupied cells by differentiating moving objects, parked cars, urban infrastructure and buildings. Another advantage of this approach is its ability to separate the drivable from the non-drivable free space. Experiments carried out in real traffic conditions with a specially equipped car illustrate the performance of this approach. A 1 2understanding capability of an autonomous vehicle in terms of drivable space characterization and mobile object detection. It is important to identify free space for path planning, but it is also important to distinguish static objects (e.g. road signs) from mobile road users (cars, cyclists, pedestrians) who are often vulnerable. If a mobile object is detected at the roadside, the vehicle can reduce its speed or modify its trajectory to avoid a possible collision. To model the vehicle environment, the approach presented here uses multiple 2D occupancy grids [3]. The information processing is based on the Dempster-Shafer theory of evidence and pignistic decisions. The map is treated as an additional source of information, on a par with the other sensors. We propose a method for incorporating this prior knowledge in an embedded perception system intended for autonomous navigation. Digital maps have been used as prior knowledge for several years already [4]. The use of drivable road maps for localization [5], [6], perception [7], navigation and driving [8] has proved to be of high added value. Geodata was also successfully used for mobile navigation in [9]. Some research works have also contributed to the map updating problem [10]. In a previous paper, we have focused on the use of highly accurate and precise special-purpose maps [11] at first. As opensourced maps such as OpenStreetMap have been successfully employed [12], [13], we propose an updated perception approach that uses this freely available map source.This paper describes a unified approach applicable to a variety of problems based on spatial representation of the environment using Dempster-Shafer theory. This theory was originally applied to occupancy grids [14], and it is only recently that researchers have made use of it in...

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Section: B Results Of Obstacle Detectionsupporting

confidence: 61%

Map-Aided Evidential Grids for Driving Scene Understanding

Kurdej

Moras²,

Cherfaoui

et al. 2015

IEEE Intell. Transport. Syst. Mag.

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“…The adoption of the city model term for 3D objects expanded and established close links over the past ten years. The main works analyze city model term in four directions: (1) the 3D city model applied to the registration information (Žiūrienė, Mešliūtė and Makutėnienė 2006); (2) mobile technology with 3D city model (Smart, Quinn and Jones 2011); (3) virtual city model (Yasumoto et al 2011 ;Cappelle et al 2012;Jahnkee, Krisp and Kumke 2011); (4) combination of photogrammetry and 3D laser scanner (Sahin et al 2012). Other studies have proposed empirical tools for architects and planners analyze and predict the flow of traffic to the public transport network (Aschwanden et al 2012) and city traffic model in transport (Pasté n et al 2012;Dawood et al 2015) .…”

Section: What Does the Literature Mention About City Model?mentioning

confidence: 99%

Fostering Entrepreneurship, Creativity and Innovation in Cities

Aranha¹,

Garcia²,

Santos³

2017

IBR

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The aim of this study is to develop a new tool to foster entrepreneurship, creativity and innovation in cit ies. The tool consists of transformative dimensions, able to synthesize, assemble and embrace the notions of entrepreneurial city, creative and innovative. Then it seeks to illustrate the operation of the city model proposed taking into account 51 Cities Master Plans in Brazil. The study is exploratory and adopts a reflexive methodology. The main innovative result is the entrepreneurial city model tool which meets five essential transformative dimensions: value proposition, customers, value configurations, strategic partnerships and revenue model. The entrepreneurial city model tool proposed aims to increase the dynamics understanding of the procedural flow in the city context, from the entrepreneurial activity perspective. This process flows dynamics can be expressed in the city, for example, in the planning, implementation and actions monitoring, programs, projects and public policies directed to notions of entrepreneurial city, creative and innovative. The innovative results of this research have several practical implications, among which are: (1) public management in the city; (2) public policy makers; (3) researchers and scholars; (4) human resource professionals.

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“…(Obst 2012) and (Peyret 2011) use 3D models to identify satellite visibility. (Ramalingam 2010), (Cappelle, 2011) and (Koch 2007) combine the use of omnidirectional camera and 3D model of the environment and match some data between the acquired image and the 3D model.…”

Section: The Caploc Conceptmentioning

confidence: 99%

Toward accurate localization in guided transport: Combining GNSS data and imaging information

Marais

Meurie

Attia

et al. 2014

Transportation Research Part C: Emerging Technologies

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Global Navigation Satellite Systems (GNSS) are widely spread (with Global Positioning System -GPS) in intelligent transport systems and offer a low cost, continuous and global solution for positioning. Unfortunately, urban users are often the most demanding of accurate localization but receive a degraded service because of signal propagation conditions. Several mitigation solutions can be developed. We propose, within CAPLOC project (2010)(2011)(2012)(2013) to deal with inaccuracy by associating image processing techniques and signal propagation knowledge. In this paper, we focus on the contribution of image processing in more accurate position estimation. Thus, we use a laboratory vehicle, which is equipped with a fisheye camera and two GNSS receivers. The camera is located on the roof and oriented upwards to capture images of the sky. The GNSS receivers are used to obtain raw data, the position of the vehicle and the reference trajectory. The proposed approach consists in determining where satellites are located in the fisheye image, and then excluding those located in non-sky regions when calculating the position. For that, the strategy is based on an image simplification step coupled with a pixels classification. The image-based exclusion procedure is compared with the classical one based on the application of a threshold on carrier-to-noise (CN0) ratio to separate LOS and NLOS signals. Accuracy improvement is satisfying with the CN0-based method and show an improvement from 13m to 4,5m. Image-based detection shows mixed improvements but promising: good in a static area and too harsh in another configuration of the scenario.

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Virtual 3D City Model for Navigation in Urban Areas

Cited by 39 publications

References 37 publications

Map-Aided Evidential Grids for Driving Scene Understanding

Map-Aided Evidential Grids for Driving Scene Understanding

Fostering Entrepreneurship, Creativity and Innovation in Cities

Toward accurate localization in guided transport: Combining GNSS data and imaging information

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