In the front-end matching process of the Cartographer algorithm, the accuracy of matching between the point cloud and submap relies on the initial values provided by the pose fusion algorithm. However, the original algorithm's pose fusion algorithm has low accuracy. To address this issue, this paper proposes an improved Cartographer algorithm based on a laser odometer. The improved algorithm utilizes NDT registration to obtain the pose transformation between frames. Additionally, a pre-integration of the IMU between the front and back frames is performed for joint optimization, allowing for the acquisition of a more accurate pose. This enhanced accuracy contributes to improving the matching of high point clouds with the submap. To analyze the efficacy of the improved algorithm, comparisons were made with the original Cartographer algorithm by analyzing the map construction effect and conducting positioning accuracy tests using datasets. The experiments confirmed that the improved algorithm is both feasible and effective in enhancing the map construction effect and pose accuracy.

Bowen Du, Xiaochang Ni, Yaqing Wang, Jie Zhou, Jing Li. Improved algorithm of cartographer based on laser odometer. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 8: 112-118. https://doi.org/10.25236/AJCIS.2023.060814.

[1] Smith R, Self M, Cheeseman P. Estimating Uncertain Spatial Relationships in Robotics[J]. Machine Intelligence & Pattern Recognition, 1988, 5(5):435-461. 

[2] Giorgio G, Cyrill S, Wolffram B. Improved Techniques for Grid Mapping with Rao-Blackwellized Particle Filters [C]//IEEE Transactions on Robotics, 2007:34-46. 

[3] Kohlbrecher S, Stryk O V, Meyer J, et al. A flexible and scalable slam system with full 3d motion estimation [C]//2011 IEEE International Symposium on Safety, Security, and Rescue Robotics IEEE, 2011:155-160. 

[4] Hess W, Kohler D, Rapp H, et al. Real-Time Loop Closure in 2D LIDAR SLAM[C]//2016 IEEE International Conference on Robotics and Automation (ICRA)IEEE, 2016:1271-1278. 

[5] Julier S J, Uhlman J K. New extension of the Kalman filter to nonlinear systems [J]. Signal Processing, Sensor Fusion, and Target Recognition VI, 1997, 3068. 

[6] Liang Z, Zhiyu L, Jingying C et al. Cartographer Algorithm and System Implementation of enhanced pose fusion for sweeping robot [J]. Journal of Software, 2020, 31(09):2678-2690. 

[7] Xin S, Huasong M. Improved Cartographer algorithm based on velocity integral pose fusion [J]. Applied Laser, 2021, 41(05):1063-1069. 

[8] Forster C, Carlone L, Dellaert F, et al. On-Manifold Preintegration for Real-Time Visual--Inertial Odometry [J]. IEEE transactions on robotics, 2017, 33(1):1-21. 

[9] Zhang J, Sanjiv S. Loam: Lidar Odometry and Mapping in Real-time. Proceedings of Robotics [C]//Science and Systems Conference, 2014:109-111. 

[10] H. Ye, Y. Chen, M. Liu. Tightly Coupled 3D LiDAR Inertial Odometry and Mapping[C]//IEEE International Conference on Robotics and Automation, 2019:3144-3150. 

[11] Zhao S, Fang Z, Li H, Scherer S. A Robust Laser-Inertial Odometry and Mapping Method for Large-Scale Highway Environments [C]//2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Macau, China, 2019:1285-1292. 

[12] Neuhaus F, Ko T, Kohnen R, et al. MC2SLAM: Real-Time Inertial Lidar Odometry Using Two-Scan Motion Compensation[C]// German Conference on Pattern Recognition. Springer, Cham, 2018:60–72. 

[13] Shan T, Englot B, Meyers D, et al. LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping [C]//2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Las Vegas, NV, USA, 2020:5135-5142. 

[14] Liguang J, Qiguang L I. Research on the calibration algorithm of disk odometer based on laser radar[J].Journal of Beijing Information Science & Technology University, 2019.