GPS World, May 2016

AUTONOMOUS NAV MAY 2016 WWW GPSWORLD COM GPS WORLD 29 better quantify the contrition of the proposed approach In practice the GPS receiver data can be used to account for dead reckoning sensor errors FIGURE 10 depicts the comparison of the estimated trajectory In the figure the RTK result is used as a reference and the dead reckoning results with and without the context dependent weighting are shown Note that when the context dependent weighting is not used the estimated trajectory in red is subject to two erroneous turns at the lower left corner and upper right corner respectively The entropy as a function of time is evaluated and shown in FIGURE 11 Note that the entropies are relatively low at 240 seconds and 1960 seconds respectively These two instants correspond to the moments when the vehicle is at the aforementioned corners Through the use of entropy based contextdependent weighting in the dead reckoning process the navigation error is significantly reduced as shown in the estimated trajectory in blue Thus the effectiveness of the proposed scheme is verified CONCLUSION For autonomous vehicle applications knowledge of the current state such as position velocity and attitude of the host vehicle are needed For robust and autonomous navigation many different sensors have been incorporated and fused to form a navigation suite In fusing different sensor data for better accuracy and integrity the quality of sensors must be considered We investigated the use of a scan matching technique for aided navigation The context of the environment in terms of the richness of features may affect the quality of the resulting navigation system To address the context dependent issue we usecd a contextdependent entropy measure to assess the quality in scan matching In addition to the increments in translation and rotation the corresponding quality indices are obtained to better blend the scan matching result into the navigation system As a result anomalous navigation results due to lack of features and improper registration can be better dealt with The proposed scheme is experimentally verified ACKNOWLEDGMENTS The work is supported by the joint NCKU ARTC research project Taiwan JYH CHING JUANG received a Ph D in electrical engineering from the University of Southern California Los Angeles He was with Lockheed Aeronautical System Company Burbank before joining the faculty of the Department of Electrical Engineering National Cheng Kung University Tainan Taiwan His research interests include sensor networks GNSS signal processing and softwarebased receivers SHANG LIN YU is an M S student in the Department of Electrical Engineering National Cheng Kung University SHUN HUNG CHEN received a Ph D from the Department of Electrical Engineering National Cheng Kung University He is with the Electronic Control Technology Group Research Development Division Automotive Research Testing Center in Taiwan His research interests include vehicle navigation and autonomous driving FIGURE 9 Test track at ARTC Taiwan FIGURE 10 Estimated trajectories FIGURE 11 Entropy as a function of time