Fast Line, Arc/Circle and Leg Detection from Laser Scan Data in a Player Driver

Xavier, J. C.; Pacheco, Moises; Castro, Daniel; Ruano, A. E.; Nunes, Urbano

doi:10.1109/robot.2005.1570721

Cited by 144 publications

(94 citation statements)

References 12 publications

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“…Geometric features have also been used by Xavier et al [7]. With a jump distance condition they split the range image into clusters and apply a set of geometric rules to each cluster to distinguish between lines, circles and legs.…”

Section: Introductionmentioning

confidence: 99%

Using Boosted Features for the Detection of People in 2D Range Data

Arras

Mozos

Burgard

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

306

252

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Abstract-This paper addresses the problem of detecting people in two dimensional range scans. Previous approaches have mostly used pre-defined features for the detection and tracking of people. We propose an approach that utilizes a supervised learning technique to create a classifier that facilitates the detection of people. In particular, our approach applies AdaBoost to train a strong classifier from simple features of groups of neighboring beams corresponding to legs in range data. Experimental results carried out with laser range data illustrate the robustness of our approach even in cluttered office environments. I. INTRODUCTIONDetecting people is a key capacity for robots that operate in populated environments. Knowledge about presence, position, and motion state of people will enable robots to better understand and anticipate intentions and actions.In this paper, we consider the problem of people detection from data acquired with laser range finders. The application of such sensors for this task has been popular in the past as they provide a large field of view and, opposed to vision, are mainly independent from ambient conditions. However, laser range data contain little information about people, especially because they typically consist of twodimensional range information. Figure 1 shows an example scan from a cluttered office environment. While this scan was recorded, several people walked through the office. The scan suggests that in cluttered environments, people detection in 2D is difficult even for humans. However, at a closer look, range measurements that correspond to humans have certain geometrical properties such as size, circularity, convexity or compactness (see Figure 2). The key idea of this work is to determine a set of meaningful scalar features that quantify these properties and to use supervised learning to create a people detector with the most informative features. In particular, our approach uses AdaBoost as a method for selecting the best features and thresholds, while at the same time creating a classifier using the selected features.In the past, many researchers focused on the problem of tracking people in range scans. One of the most popular approach in this context is to extract legs by the detecting moving blobs that appear as local minima in the range image [1], [2], [3], [4]. To this end, two types of features have been quite popular: motion and geometry features. Motion in range data is typically identified by subtracting two subsequent scans. If the robot is moving itself, the scans have first to be aligned, e.g., using scan matching. The drawback of motion features is that only moving people can be found. Topp and Christensen [5] extend the method of Schulz et

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Section: Introductionmentioning

confidence: 99%

Using Boosted Features for the Detection of People in 2D Range Data

Arras

Mozos

Burgard

2007

Proceedings 2007 IEEE International Conference on Robotics and Automation

306

252

View full text Add to dashboard Cite

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“…In our system, after extracting a foot, the data of a foot must be analyzed to nd the circular portion and the linear portion. A fast method to calculate the line and the circle using the laser range scanner data proposed in [24] is applicable.…”

Section: Related Workmentioning

confidence: 99%

A Walking Training System with Customizable Trajectory Designing

Dong

Matsumaru

2014

Paladyn, Journal of Behavioral Robotics

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This paper shows a novel walking training system for foot-eye coordination. To design customizable trajectories for di erent users conveniently in walking training, a new system which can track and record the actual walking trajectories by a tutor and can use these trajectories for the walking training by a trainee is developed. We set the four items as its human-robot interaction design concept: feedback, synchronization, ingenuity and adaptability. A foot model is proposed to de ne the position and direction of a foot. The errors in the detection method used in the system are less than 40 mm in position and 15 deg in direction. On this basis, three parts are structured to achieve the system functions: Trajectory Designer, Trajectory Viewer and Mobile Walking Trainer. According to the experimental results, we have con rmed the system works as intended and designed such that the steps recorded in Trajectory Designer could be used successfully as the footmarks projected in Mobile Walking Trainer and foot-eye coordination training would be conducted smoothly.

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“…Available methods so far, approach the problem of detecting geometric features such as lines, circles, legs by manual design and threshold hand-tuning [9]. Moreover, the extracted features are compared to objects stored in a database to recognize targets [10].…”

Section: Introductionmentioning

confidence: 99%

Learning moving objects in a multi-target tracking scenario for mobile robots that use laser range measurements

Kondaxakis

Baltzakis

Trahanias

2009

2009 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-This paper addresses the problem of real-time moving-object detection, classification and tracking in populated and dynamic environments. In this scenario, a mobile robot uses 2D laser range data to recognize, track and navigate moving targets. Most previous approaches either rely on pre-defined data features or off-line training of a classifier for specific data sets, thus eliminating the possibility to detect and track different-shaped moving objects. We propose a novel and adaptive technique where potential moving objects are classified and learned in real-time using a Fuzzy ART neural network algorithm. Experimental results indicate that our method can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape.

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Fast Line, Arc/Circle and Leg Detection from Laser Scan Data in a Player Driver

Cited by 144 publications

References 12 publications

Using Boosted Features for the Detection of People in 2D Range Data

Using Boosted Features for the Detection of People in 2D Range Data

A Walking Training System with Customizable Trajectory Designing

Learning moving objects in a multi-target tracking scenario for mobile robots that use laser range measurements

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