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1 Vol. 50 No (Jan. 2009) GPS GPS Real Time Kinematic-GPS GPS SLAM Simultaneous Localization And Mapping Precise Ego-localization Estimation Method by Integration Using Standard GPS and an Onboard Scanning Laser Radar Yoshiko Kojima, 1 Arata Takahashi 1 and Yoshiki Ninomiya 1 Various driver assistance systems have been developed, and some functionality that enables the estimation of precise ego-localization is required. Standard GPS is widely used for a vehicle navigation system. However, the positional accuracy of standard GPS is not adequate for the utilization. The number of onboard outside-monitoring sensors such as Scanning Laser Radar has been increasing in these years. Therefore, this paper proposes an estimation method of precise ego-localization by using an onboard outside-monitoring sensor and standard GPS. Proposed method applies SLAM (Simultaneous Localization And Mapping) to actual road environment, and the method enables sequential estimation of precise ego-localization by optimizing both of localization and mapping. Furthermore, the effectiveness of the proposed method is evaluated based on simulated data, actual data, and a survey road environment , ) 1m RTK-GPS Real Time Kinematic-GPS 2),3) RTK-GPS cm RTK-GPS GPS m 50 m GPS 10 cm 1 Toyota Central R&D Laboratories, Inc. 64 c 2009 Information Processing Society of Japan
2 65 GPS GPS GPS SLAM Simultaneous Localization And Mapping 4) RTK-GPS ) 6),7) GPS IMU Inertial Measurement Unit GPS 8),9) 3 Bundle Adjustment SLAM SLAM 80 10),11) SLAM SLAM 12) 13) SLAM SLAM GPS SLAM GPS GPS GPS
3 66 GPS Fig. 2 2 Estimation procedure. 1 Fig. 1 Proposed method. GPS 3.2 GPS GPS 1 2 Δt s GPS Δt g 2 step1 GPS GPS step2 Δt s step1 step1 step2 GPS step2 GPS GPS step2 step2 3.3 SLAM EKF Extended Kalman Filter 14),15) PF Particle Filter 16),17) EKF UKF Unscented Kalman Filter 18),19) PF RBPF Rao-Blackwellised Particle Filter 20),21) SLAM SLAM EKF EKF
4 67 GPS ˆx k k =ˆx k k 1 + K k (y k h(ˆx k k 1 )) (1) ˆΣ k k = ˆΣ k k 1 K k H k ˆΣk k 1 (2) ˆx k+1 k = f(ˆx k k ) (3) K k = ˆΣ k k 1 H T k (H k ˆΣk k 1 H T k +Σv k ) 1 (4) ˆΣ k+1 k = F k ˆΣk k F T k G k Σw k G T k (5) x =(X vo,z vo,φ v,v v,ω v,x L1,Z L1,...,X LN,Z LN ) (6) 3 Fig. 3 Coordinate system. 3 (1) (5) (1) (2) (3) (5) EKF (6) (7) x (Xvo,Zvo) φv Vv ωv (X Li,Z Li) y GPS 22) (Xg,Zg) Vg Li rs Li φs Li f (8) h (9) EKF F H (8) f (9) h (10) (11) N (X Li,Z Li) (rs Li,φs Li) y =(X g,z g,v g,r sl1,φ sl1,...,r sln,φ sln ) (7) X vo(t +Δt) =X vo(t)+v v(t) sin φ v(t) Δt f: Z vo(t +Δt) =Z vo(t)+v v(t) cos φ v(t) Δt (8) φ v(t +Δt) =φ v(t)+ω v(t) Δt h: X g = X vo Z g = Z vo V g = V v r sli = (X Li X vo) 2 +(Z Li Z vo) 2 φ sli =tan 1 (X Li X vo/z Li Z vo) φ v F = f(ξ k) ξ k H = h(ξ k) ξ k ξ k =ˆx k k 1 (10) 4. ξ k =ˆx k k 1 (11) (9)
5 68 GPS 4.1 GPS GPS 4 1 GPS σ σ EKF 1 σ GPS Fig. 5 5 ( 50 m The result of estimation error on the traveling direction (Landmark Interval: 50 m) m Fig. 4 An example of supposed conditions. Table 1 1 Supposed conditions and sensor performance. Fig m The result of estimation error on the traveling direction (Landmark Interval: 500 m).
6 69 GPS Table 2 2 2σ Estimation error evaluation by proposed method. 3 2σ Table 3 Estimation error evaluation by previous work. Fig σ Estimation error distribution (proposed method). 8 2σ Fig. 8 Estimation error distribution (previous work). GPS σ 3m 50 m m 6 50 m 5 5 GPS m GPS GPS σ ±2σ 95.4% 2σ 2 7
7 70 GPS m 500 m GPS 100 m 50 m 1 GPS σ 15 m GPS σ 3m 50 m 2σ 1m 3 8 1m GPS GPS σ 3m 2σ <1m 30 m 2 GPS 1 1m 30 m m RIEGL 4 Table 4 Specification table of scanning laser radar. 9 Fig. 9 An example of actual scene. LMS-Q140i-80 GPS Pioneer navicom GPS-M1zz RTK-GPS Trimble 5700 RTK-GPS 4 9 9
8 71 GPS Fig Data acquisition environment. 400 m m 9 EKF 20 Hz GPS GPS GPS GPS 10 m σ 2.8 m 11 GPS 5 GPS 2σ 5.57 m 1m 31.1% 2σ 1.57 m 1m 84.7% 11 Fig. 11 Estimation result by actual data. 5 Table 5 Traveling directional estimation error of the ego-localization m 50 m m m m m GPS σ 2.8 m 7 2σ 1.57 m
9 72 GPS 2σ m m SLAM km km km 1.3 km 26 km 12 GPS m ±2σ 95% 32 m 95% 1 12 Fig. 12 Roads for actual environment survey. 13 Fig. 13 Survey result of actual landmark interval. GPS σ 3 7m 6. GPS
10 73 GPS SLAM GPS GPS 1) 19 (2008). 2) Misra, P. and Enge, P. GPS GPS (2004). 3) Talbot, C.N.: Centimeters in the Field, a Users Perspective of Real Time Kinematic Positioning in a Production Environment, Proc. ION GPS-93, Salt Lake City, U.S.A., pp (Sep. 1993). 4) GPS ITS , Vol.106, No.181 (2006). 5) Thorsten,W., Nico, K. and Dietmayer, K.: Precise Ego-Localization in Urban Area using Laserscanner and High Accuracy Feature Maps, Proc. IEEE Intelligent Vehicle Symposium 2005, Las Vegas, U.S.A., pp (Jun. 2005). 6) Bonnifait, P., Jabbour, M. and Cherfaoui, V.: Integrated Navigation using GISbased Information, European Journal of Navigation, Vol.5, No.1, pp.2 7 (2007). 7) Bonnifait, P., Jabbour, M. and Cherfaoui, V.: Autonomous navigation in urban areas using GIS-managed information, International Journal Vehicle Autonomous Systems, Vol.6, No.1/2 pp (2008). 8) GPS Vol.47, No.SIG 5 (CVIM13) pp (2006). 9) Zhao, H. and Shibasaki, R.: Updating A Digital Geographic Database using Vehicle-borne Laser Scanners and Line Cameras, Photogrammetric Engineering & Remote Sensing, Vol.71, No.4, pp (2005). 10) Thrun, S., Burgard, W. and Fox, D.: A Probabilistic Approach to Concurrent Mapping and Localization for Mobile Robots, Machine Learning, Vol.31, pp (1998). 11) Thrun, S., Burgard, W. and Fox, D.: Probabilistic ROBOTICS, TheMITPress (2005). 12) Vol.23, No.2, pp (2005). 13) Wang, C.C., Thorpe, C., Thrun, S., Hebert, M. and Durrant-Whyte, H.: Simultaneous Localization, Mapping and Moving Object Tracking, The International Journal of Robotics Research, Vol.29, Issue 9, pp (2007). 14) Williams, B.S., Durrant-Whyte, H. and Sissanayake, G.: Constrained Initialization of the Simultaneous Localization and Mapping Algorithm, The International Journal of Robotics Research, Vol.22, No.7-8, pp (2003). 15) RISTIC, B.: Beyond the Kalman Filter Particle Filters for Tracking Applications, Artech House Publishers, Boston, London (2004). 16) Fox, D., Burgard, W., Dellaert, F. and Thrun, S.: Monte Carlo Localization: Efficient Position Estimation for Mobile Robots, Proc. 16th National Conf. Artificial Intelligence, Orland, FL., U.S.A., pp (1999). 17) Vol.23, No.4, pp (2005). 18) : Unscented Kalman Filter Vol.56, No.649, pp (2008). 19) Ko, J., Klein, J.D., Fox, D. and Haehnel, D.: GP-UKF: Unscented Kalman Filters with Gaussian Process Prediction and Observation Models, Proc IEEE/RSJ International Conf. on Intelligent Robots and Systems, San Diego, CA., U.S.A., pp (Oct. 2007). 20) Doucet, A., Freitas, N., Murphy, K. and Russel, S.: Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks, Uncertainty in Artificial Intelligence (2000). 21) Stachniss, C., Grisetti, G. and Burgard, W.: Recovering Particle Diversity in a Rao-Blackwellized Particle Filter for SLAM After Activity Closing Loops, Proc IEEE International Conf. on Robotics and Automation, Barcelona, Spain, pp (Apr. 2005). 22)
11 74 GPS ( ) ( )
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