**4. Experiments**

After the modifications mentioned previously, now, we have a testbed that is being used for testing the autonomous driving algorithms such as simultaneous localization and mapping (SLAM), trajectory planning, trajectory tracking, and low-level controllers. Until now, using the designed testbed, the map of the test environment is constructed using "gmapping" library [16] in ROS development environment. **Figure 8** shows a part of the environment where the mapping and localization algorithms are tested.

**Figure 8.** *Real test environment.*

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**Figure 11.**

*Localization performance of the wheelchair.*

**Figure 10.**

*Conversion of a Conventional Wheelchair into an Autonomous Personal Transportation Testbed*

**Figure 9** illustrates the real map of the environment which is drawn using AutoCAD® software using accurate manual measurements and the constructed map drawn by the autonomous wheelchair using SLAM. The famous SLAM package of ROS named as "gmapping" is used for map construction. The map is based on SICK LMS-151 Lidar and the odometry information. As it is seen from **Figures 10** and **11**, the constructed map and the real map are almost same. Since the office doors are closed in the AutoCAD version, the constructed map for comparison is prepared in the same condition. According to the structural similarity index method (SSIM) [17], when the window size is taken as 5 × 5 pixels, the similarity of two maps is 94%.

*Adaptive particle filter localization application (blue arrows: particle poses, yellow: real pose).*

*DOI: http://dx.doi.org/10.5772/intechopen.93117*

*Conversion of a Conventional Wheelchair into an Autonomous Personal Transportation Testbed DOI: http://dx.doi.org/10.5772/intechopen.93117*

**Figure 9** illustrates the real map of the environment which is drawn using AutoCAD® software using accurate manual measurements and the constructed map drawn by the autonomous wheelchair using SLAM. The famous SLAM package of ROS named as "gmapping" is used for map construction. The map is based on SICK LMS-151 Lidar and the odometry information. As it is seen from **Figures 10** and **11**, the constructed map and the real map are almost same. Since the office doors are closed in the AutoCAD version, the constructed map for comparison is prepared in the same condition. According to the structural similarity index method (SSIM) [17], when the window size is taken as 5 × 5 pixels, the similarity of two maps is 94%.

#### **Figure 10.**

*Service Robotics*

**4. Experiments**

localization algorithms are tested.

providing only the goal point, the user can set the waypoints to be tracked, by touching the desired coordinates on the map. Additional features of this interface are to send an emergency signal to the chair and to see the critical information such as wheelchair's velocity and actual position on map. The interface software is designed on a touchable tablet PC. Assembled form of the tablet on wheelchair and

a screenshot from the designed interface software are shown in **Figure 7**.

*Real map drawn using AutoCAD® software (left) and constructed map by SLAM (right).*

After the modifications mentioned previously, now, we have a testbed that is being used for testing the autonomous driving algorithms such as simultaneous localization and mapping (SLAM), trajectory planning, trajectory tracking, and low-level controllers. Until now, using the designed testbed, the map of the test environment is constructed using "gmapping" library [16] in ROS development environment. **Figure 8** shows a part of the environment where the mapping and

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**Figure 9.**

**Figure 8.**

*Real test environment.*

*Adaptive particle filter localization application (blue arrows: particle poses, yellow: real pose).*

**Figure 11.** *Localization performance of 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 localization.

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 measurements from the wheelchair.

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