**Author details**

*Service Robotics*

localization.

**5. Conclusion**

the next studies.

**Acknowledgements**

Council (TUBITAK) under project no. 118E809.

as well.

measurements from the wheelchair.

Another application is global localization of the wheelchair. In order to plan a path and track it, an autonomous agent must be aware of its pose in the map. Adaptive Monte Carlo Localization (AMCL), which is based on particle filter, is used for pose estimation. The localization algorithm is run separately after the map construction. This time we use a map that was constructed when the doors are open since the localization tests were done in open-door condition. **Figure 10** shows the poses of particles (blue) and the real pose (yellow) during wheelchair's motion. It should be noted that the wheelchair is driven manually for both mapping and

In **Figure 10**, the figure in the left shows the beginning phase, and all the particles are scattered into environment uniformly. No prior information is provided as initial pose. The figure in the middle illustrates that the particles concentrated on places coherent with the Lidar and odometry measurements. Finally in the right figure, it is shown that particles are gathered around the real position of the wheelchair after 9 seconds. The real position of the wheelchair is calculated by the manual

**Figure 11** shows the localization performance of the wheelchair in the same real environment. It is observed that the average localization error after the points are

In this study, the conversion procedure of a conventional electric wheelchair into an autonomous personal transportation testbed is described in detail. The conversion process is investigated under two main sections. The first part is by-wire conversion that allows the wheelchair to be controlled via digital commands. The second part includes the studies on the sensors, computational system and human-machine interface. Sensors and computational hardware that have different characteristics are included in the system for comparison and optimization of their performance in the future. The platform was tested using SLAM and AMCL algorithms for mapping and localization successfully. It is observed that the constructed map using industrial LIDAR and odometry data is almost the same with the real map and localization performance is acceptable for

In the future, we plan to use the wheelchair as a research platform to further improve the autonomous personal transportation algorithms that will be used in narrow and cluttered environments. Comparison of localization and mapping performance of different methods and sensors in such environments will be studied

This work was supported by the Turkish Scientific and Technological Research

converged is below 10 cm, which is acceptable for our future studies.

**88**

Volkan Sezer\*, Rahman Salim Zengin, Hosein Houshyari and Murat Cenk Yilmaz Istanbul Technical University, Istanbul, Turkey

\*Address all correspondence to: sezerv@itu.edu.tr

© 2020 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
