Laser and Vision Based Outdoor Object Mapping

Douillard, Bertrand; Fox, Dieter; Ramos, Fábio

doi:10.15607/rss.2008.iv.002

Cited by 37 publications

(29 citation statements)

References 19 publications

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“…However, in laser scanning points that are irregularly distributed, the definition of adjacent relations is not straightforward. In previous studies using point data, neighborhood relations are defined by Delaunay Triangulation (DT) [25], k nearest neighbors [6] and super-voxels [19]. In our study, we define two different neighboring systems for establishing the short-range and long-range relation.…”

Section: Definition Of the Graphmentioning

confidence: 99%

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

et al. 2016

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Abstract:Railways have been used as one of the most crucial means of transportation in public mobility and economic development. For safe railway operation, the electrification system in the railway infrastructure, which supplies electric power to trains, is an essential facility for stable train operation. Due to its important role, the electrification system needs to be rigorously and regularly inspected and managed. This paper presents a supervised learning method to classify Mobile Laser Scanning (MLS) data into ten target classes representing overhead wires, movable brackets and poles, which are key objects in the electrification system. In general, the layout of the railway electrification system shows strong spatial regularity relations among object classes. The proposed classifier is developed based on Conditional Random Field (CRF), which characterizes not only labeling homogeneity at short range, but also the layout compatibility between different object classes at long range in the probabilistic graphical model. This multi-range CRF model consists of a unary term and three pairwise contextual terms. In order to gain computational efficiency, MLS point clouds are converted into a set of line segments to which the labeling process is applied. Support Vector Machine (SVM) is used as a local classifier considering only node features for producing the unary potentials of the CRF model. As the short-range pairwise contextual term, the Potts model is applied to enforce a local smoothness in the short-range graph; while long-range pairwise potentials are designed to enhance the spatial regularities of both horizontal and vertical layouts among railway objects. We formulate two long-range pairwise potentials as the log posterior probability obtained by the naive Bayes classifier. The directional layout compatibilities are characterized in probability look-up tables, which represent the co-occurrence rate of spatial relations in the horizontal and vertical directions. The likelihood function is formulated by multivariate Gaussian distributions. In the proposed multi-range CRF model, the weight parameters to balance four sub-terms are estimated by applying the Stochastic Gradient Descent (SGD). The results show that the proposed multi-range CRF can effectively classify individual railway elements, representing an average recall of 97.66% and an average precision of 97.07% for all classes.

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Section: Definition Of the Graphmentioning

confidence: 99%

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

et al. 2016

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“…A combination of camera and laser measurements has been used to detect vegetation in several approaches [8,10,11,12]. In a combined system, Wellington et al [12] use the remission value of a laser scanner in addition to density features and camera images as a classification feature.…”

Section: Related Workmentioning

confidence: 99%

Identifying vegetation from laser data in structured outdoor environments

Wurm¹,

Kretzschmar²,

Kümmerle³

et al. 2014

Robotics and Autonomous Systems

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The ability to reliably detect vegetation is an important requirement for outdoor navigation with mobile robots as it enables the robot to navigate more efficiently and safely. In this paper, we present an approach to detect flat vegetation, such as grass, which cannot be identified using range measurements. This type of vegetation is typically found in structured outdoor environments such as parks or campus sites. Our approach classifies the terrain in the vicinity of the robot based on laser scans and makes use of the fact that plants exhibit specific reflection properties. It uses a support vector machine to learn a classifier for distinguishing vegetation from streets based on laser reflectivity, measured distance, and the incidence angle. In addition, it employs a vibration-based classifier to acquire training data in a self-supervised way and thus reduces manual work. Our approach has been evaluated extensively in real world experiments using several mobile robots. We furthermore evaluated it with different types of sensors and in the context of mapping, autonomous navigation, and exploration experiments. In addition, we compared it to an approach based on linear discriminant analysis. In our real world experiments, our approach yields a classification accuracy close to 100%.

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“…In particular, they can not model the fact that the label l i of a given laser segment S i is more likely to be l j if we know that l j is the label of S j and that S j and S i are neighbors. One way to model this conditional dependency is to use Conditional Random Fields (CRFs) , as shown by Douillard et al (2008). CRFs represent the conditional probability p(l | s) using an undirected cyclic graph, in which each node is associated with a hidden random variable l i and an observation s i .…”

Section: Object and People Detection From 2d Laser Datamentioning

confidence: 99%

Awareness of Road Scene Participants for Autonomous Driving

Petrovskaya

Perrollaz

Oliveira

et al. 2012

Handbook of Intelligent Vehicles

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This chapter describes detection and tracking of moving objects (DATMO) for purposes of autonomous driving. DATMO provides awareness of road scene participants, which is important in order to make safe driving decisions and abide by the rules of the road. Three main classes of DATMO approaches are identified and discussed. First is the traditional approach, which includes data segmentation, data association, and filtering using primarily Kalman filters. Recent work within this class of approaches has focused on pattern recognition techniques. The second class is the model based approach, which performs inference directly on the sensor data without segmentation and association steps. This approach utilizes geometric object models and relies on non-parametric filters for inference. Finally, the third class is the grid based approach, which starts by constructing a low level grid representation of the dynamic environment. The resulting representation is immediately useful for determining free navigable space within the dynamic environment. Grid construction can be followed by segmentation, association, and filtering steps to provide object level representation of the scene. The chapter introduces main concepts, reviews relevant sensor technologies, and provides extensive references to recent work in the field. The chapter also provides a taxonomy of DATMO applications based on road scene environment and outlines requirements for each application.

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Laser and Vision Based Outdoor Object Mapping

Cited by 37 publications

References 19 publications

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

Multi-Range Conditional Random Field for Classifying Railway Electrification System Objects Using Mobile Laser Scanning Data

Identifying vegetation from laser data in structured outdoor environments

Awareness of Road Scene Participants for Autonomous Driving

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