People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters

@article{Schulz2003PeopleTW,
  title={People Tracking with Mobile Robots Using Sample-Based Joint Probabilistic Data Association Filters},
  author={Dirk Schulz and Wolfram Burgard and Dieter Fox and Armin B. Cremers},
  journal={The International Journal of Robotics Research},
  year={2003},
  volume={22},
  pages={116 - 99}
}
One of the goals in the field of mobile robotics is the development of mobile platforms which operate in populated environments. For many tasks it is therefore highly desirable that a robot can track the positions of the humans in its surrounding. In this paper we introduce sample-based joint probabilistic data association filters as a new algorithm to track multiple moving objects. Our method applies Bayesian filtering to adapt the tracking process to the number of objects in the perceptual… Expand
Tracking multiple moving objects for mobile robotics navigation
TLDR
A method for detection and tracking of multiple moving objects using sensor information, which uses particle filters to estimate objects states, and sample based joint probabilistic data association filters to perform the assignment of features detected from sensor data to filters. Expand
Learning moving objects in a multi-target tracking scenario for mobile robots that use laser range measurements
TLDR
Experimental results indicate that the proposed Fuzzy ART neural network algorithm can effectively distinguish and track moving targets in cluttered indoor environments, while at the same time learning their shape. Expand
Tracking multiple moving objects in a dynamic environment for autonomous navigation
  • J. Almeida, R. Araújo
  • Computer Science
  • 2008 10th IEEE International Workshop on Advanced Motion Control
  • 2008
TLDR
The robot is capable of detect and track several moving objects in its surroundings, using only a range finder sensor, using major frameworks that enable the presented method are particle filters and Sample-based Joint Probabilistica Data Association Filters. Expand
Active Localization of People with a Mobile Robot Based on Learned Motion Behaviors
Mobile robots that provide service to people can carry out their tasks more efficiently if they know where the people are. In this paper we present an approach to actively maintain a probabilisticExpand
Real-Time Tracking of Moving Objects Using Particle Filters
Mobile robots and vehicles are increasingly used in dynamic environments populated by humans and other moving objects and vehicles. In this context, tracking of surrounding moving objects isExpand
Multiple Hypothesis Tracking of Clusters of People
  • M. Mucientes, W. Burgard
  • Computer Science
  • 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2006
TLDR
An algorithm for tracking clusters of people using multiple hypothesis tracking (MHT) that can robustly deal with several groups of people and is able to reliably manage the splits and joins of clusters. Expand
Computationally efficient solutions for tracking people with a mobile robot: an experimental evaluation of Bayesian filters
TLDR
Three efficient implementations of multisensor-human tracking based on different Bayesian estimators, which show that a solution based on the UKF can perform as good as particle filters and can be often a better choice when computational efficiency is a key issue. Expand
Tracking multiple targets from a mobile robot platform using a laser range scanner
A major issue in the field of mobile robotics today is the detection and tracking of moving objects (DATMO) from a moving observer. In dynamic and highly populated environments, this problem presentsExpand
People tracking with a mobile robot: a comparison of Kalman and particle filters
TLDR
This paper compares three different Bayesian estimators to perform people tracking with a mobile robot using sensor fusion and shows that the UKF can perform as well as a particle filter but at a much lower computational cost. Expand
Map Based Human Motion Prediction for People Tracking*
  • F. Beck, M. Bader
  • Computer Science
  • 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
  • 2019
TLDR
This work presents an approach to tracking people from a mobile robot platform, incorporating a novel approach to human motion prediction, embedded into a particle-filter based tracking approach designed for the use on a moving platform and able to incorporate a variety of person detectors. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 95 REFERENCES
Conditional particle filters for simultaneous mobile robot localization and people-tracking
Presents a probabilistic algorithm for simultaneously estimating the pose of a mobile robot and the positions of nearby people in a previously mapped environment. This approach, called theExpand
Map building with mobile robots in populated environments
TLDR
This paper uses a probabilistic method to track multiple people and to incorporate the results of the tracking technique into the mapping process, which results in more accurate maps. Expand
Tracking People from a Mobile Platform
TLDR
This work applies a vision-based approach using real-time stereo and 3D reconstruction, that explicitly models both foreground and background objects in an efficient manner, and updates a background occupancy map based on robot motion. Expand
An experimental comparison of localization methods
  • Jens-Steffen Gutmann, W. Burgard, D. Fox, K. Konolige
  • Engineering, Computer Science
  • Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)
  • 1998
TLDR
This experimental study compares two methods for localization of indoor mobile robots: Markov localization, which uses a probability distribution across a grid of robot poses; and scan matching, which using Kalman filtering techniques based on matching sensor scans. Expand
Monte Carlo localization for mobile robots
TLDR
The Monte Carlo localization method is introduced, where the probability density is represented by maintaining a set of samples that are randomly drawn from it, and it is shown that the resulting method is able to efficiently localize a mobile robot without knowledge of its starting location. Expand
A region-based approach for cooperative multi-target tracking in a structured environment
  • Boyoon Jung, G. Sukhatme
  • Engineering, Computer Science
  • IEEE/RSJ International Conference on Intelligent Robots and Systems
  • 2002
TLDR
A region-based approach which controls robot deployment at two levels is introduced, which suggests that an optimal ratio of robots to stationary sensors may exist for a given environment with certain occlusion characteristics. Expand
Camera-based monitoring system for mobile robot guidance
  • E. Kruse, F. Wahl
  • Engineering, Computer Science
  • Proceedings. 1998 IEEE/RSJ International Conference on Intelligent Robots and Systems. Innovations in Theory, Practice and Applications (Cat. No.98CH36190)
  • 1998
TLDR
The paper presents concepts and components of an experimental system: Sensing is done with CCD cameras; the processed data is furnished to different modules regarding robot localization, obstacle detection and motion planning. Expand
Laser-Based People Tracking
In this paper, we describe a method for real-time tracking of ob- jects with multiple laser range-finders covering a workspace in a par- allel and distributed fashion. Tracking people is a popularExpand
Hybrid, high-precision localisation for the mail distributing mobile robot system MOPS
  • K. Arras, S. J. Vestli
  • Computer Science
  • Proceedings. 1998 IEEE International Conference on Robotics and Automation (Cat. No.98CH36146)
  • 1998
TLDR
The new localisation algorithms under implementation for the mail distributing mobile robot, MOPS, of the Institute of Robotics, Swiss Federal Institute of Technology Zurich, employ consistent probabilistic feature extraction, clustering, matching and estimation of the vehicle position and orientation. Expand
Auxiliary particle filter robot localization from high-dimensional sensor observations
  • N. Vlassis, B. Terwijn, B. Kröse
  • Engineering, Computer Science
  • Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292)
  • 2002
TLDR
This work proposes the use of an inverted nonparametric observation model computed by nearest neighbor conditional density estimation that can lead to a fully adapted optimal filter, and is able to successfully handle image occlusion and robot kidnap. Expand
...
1
2
3
4
5
...