Fast, reliable, adaptive, bimodal people tracking for indoor environments

Scheutz, Matthias; Mcraven, J.; Cserey, G.

doi:10.1109/iros.2004.1389583

Cited by 58 publications

(39 citation statements)

References 14 publications

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“…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.…”

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

show abstract

“…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 [Fod et al, 2002;Kleinhagenbrock et al, 2002;Scheutz et al, 2004;Schulz et al, 2003a]. To this end, two types of features have been quite popular: motion and geometry features.…”

Section: Related Workmentioning

confidence: 99%

Semantic Labeling of Places with Mobile Robots

Mozos

2010

Springer Tracts in Advanced Robotics

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Zusammenfassung SummaryIndoor environments can typically be divided into places with different functionalities like corridors, rooms or doorways. The ability to learn such semantic categories from sensor data enables a mobile robot to extend the representation of the environment, and to improve its capabilites. As an example, natural language terms like corridor or room can be used to communicate the position of the robot in a more intuitive way. Other tasks, like exploration or localization, can also be carried out by the robot in a better way when semantic information is taken into account.In this thesis, we present a method that enables a mobile robot to classify the different places of indoor environments into semantic classes, and then use this information to extend its representations of the environments. The main idea is to classify the position of the robot based on the current observations taken by the robot. In this work, we use as main observations the scans obtained from a laser range sensor. Each scan is represented by a set of features that encode the geometrical properties of the current position. These features are then used to classify the scan into the corresponding semantic class. The output of the classification is represented by a probability distribution over the set of possible semantic classes. This probabilistic representation permits us to apply further probabilistic techniques to improve the final classification, reducing the number of errors. We also present an extension which enables the robot to include other types of observations in the classification, like camera images.This work additionally introduces several applications of the previous approach in different robotic tasks. First, we will show how the semantic information can be used to extract topological maps from indoor environments. In a second application, we present a method that incorporates transitions between different places when classifying a trajectory taken by a mobile robot. It will also be shown that the semantic information can reduce the time needed by the robot in exploration and localization tasks. Finally, we present the semantic classification of places as part of an integrated robotic system designed for interacting with humans using natural language.

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“…In order to reduce uncertainty and improve redundancy, two or more sensors can be integrated for human detection and recognition. Heuristic approaches are in some cases used to estimate the position of a person with mobile robots equipped with laser and camera [38]. These sensors can be combined to reduce the searching area during human detection, using the laser to find the direction of possible targets and applying face detection only to small portions of the current frame [13].…”

Section: Related Workmentioning

confidence: 99%

A Bank of Unscented Kalman Filters for Multimodal Human Perception with Mobile Service Robots

Bellotto

2010

Int J of Soc Robotics

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A new generation of mobile service robots could be ready soon to operate in human environments if they can robustly estimate position and identity of surrounding people. Researchers in this field face a number of challenging problems, among which sensor uncertainties and real-time constraints. In this paper, we propose a novel and efficient solution for simultaneous tracking and recognition of people within the observation range of a mobile robot. Multisensor techniques for legs and face detection are fused in a robust probabilistic framework to height, clothes and face recognition algorithms. The system is based on an efficient bank of Unscented Kalman Filters that keeps a multi-hypothesis estimate of the person being tracked, including the case where the latter is unknown to the robot. Several experiments with real mobile robots are presented to validate the proposed approach. They show that our solutions can improve the robot's perception and recognition of humans, providing a useful contribution for the future application of service robotics.

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Fast, reliable, adaptive, bimodal people tracking for indoor environments

Cited by 58 publications

References 14 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

Semantic Labeling of Places with Mobile Robots

A Bank of Unscented Kalman Filters for Multimodal Human Perception with Mobile Service Robots

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