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Simultaneous people tracking and robot localization in dynamic social spaces

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Abstract

Accurate robot localization and people tracking are necessary for deploying service robots in crowded everyday environments such as shopping malls, and features like product displays change over time, making map-based localization using on-board sensors difficult. We propose the use of an external sensor system to track people together with one or more robots. This approach is more robust to occlusions than on-board sensing and is unaffected by changing map features. In our system, laser range finders track people and robots in the environment, and odometry data is used to associate each robot with a tracked entity and correct the robot’s pose. Techniques are also presented for identifying and recovering from tracking errors. Simulation results show that our system can outperform localization using on-board sensors, both in tracking accuracy and in automatic recovery from errors. We demonstrate our system’s effectiveness in simulation, in a controlled experiment in a real shopping mall environment, and in real human–robot interactions with customers in a busy shopping arcade.

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Notes

  1. http://software-and-algorithms.blogspot.jp/2012/09/the-hungarian-algorithm-for-assignment.html

  2. 3DTK — The 3D Toolkit. http://slam6d.sourceforge.net/. Accessed July 22, 2013

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Acknowledgments

Thanks to the staff of Universal CityWalk Osaka and Apita Town Keihanna for their cooperation in this research, and thanks to Dr. Satoshi Koizumi for his assistance in organizing the field trials. This research was supported in part by the Ministry of Internal Affairs and Communications of Japan, and in part by JST, CREST.

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Correspondence to Dylan F. Glas.

Appendix

Appendix

Most mapping and localization approaches for mobile robots rely on the assumption that environments are static and do not change over time. Many dynamic obstacles such as pedestrians were present in our shopping mall environment, necessitating a method for removing moving objects from the robot’s scan data. The next subsection explains this approach.

1.1 Obstacle removal

First we process each scan of laser data, grouping points into clusters. When two consecutive points are within 0.1 m of distance they are grouped as a cluster. A covariance matrix is built from the point distribution of each cluster and the eigenvalues of the matrix are computed to analyze the shape of the cluster. If the length of a cluster is smaller than 0.5 m and the cluster distribution does not represent a straight line then it is determined that the cluster is a potential moving obstacle (one or two human legs together, a scan of a typical baby cart, or one of the simulated humans described in Sect. 5) and it is removed from the scan data. With this method small moving obstacles can be erased from the scan data for map building and localization. The drawback is that some small clusters which are part of the environment are removed as well; however, the noisy moving measurements are erased from the scene, improving the resulting map and the localization performance.

1.2 Map building

As many variations of SLAM exist in the field of robot navigation, we aimed to use commonly-available tools and techniques for our comparison.

Map generation was performed by driving the robot slowly through the environment, avoiding frequent turns whenever possible. In our algorithm, we first erased from the laser scans small features, such as human legs, shopping carts, and baby strollers from the raw data, based on the cluster analysis criteria explained in the previous subsection. The laser scan and odometry data from the robot was recorded for this map-building run, and a raster map was built offline by an ICP-based SLAM to correct the trajectory of the robot and align the scans (Borrmann et al. 2008) using 3DTK SLAM software using 2D data (see footnote 2). With the resulting data we built a grid map (Moravec and Elfes 1985; Elfes 1989) with the same coordinate system as the human tracking system.

1.3 Localization technique

To generate map-matching localization estimates for the experiment trials, we replayed laser scan data and odometry data in an offline simulator. Based on this data, our localization algorithm used a particle filter to estimate the robot’s position. The particle filter estimates the robot position with a weighted set of particles. The position of each particle is estimated using wheel encoder data. Particle likelihoods are updated using the previously constructed grid map with a ray tracing approach (Fox 2003; Fox et al. 1999). In our implementation, we used 200 particles for the robot position estimation.

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Glas, D.F., Morales, Y., Kanda, T. et al. Simultaneous people tracking and robot localization in dynamic social spaces. Auton Robot 39, 43–63 (2015). https://doi.org/10.1007/s10514-015-9426-3

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Navigation