Abstract
Reducing the cumulative error in the process of simultaneous localization and mapping (SLAM) has always been a hot issue. In this paper, in order to improve the localization and mapping accuracy of ground vehicles, we proposed a novel optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection. We only used the lidar point cloud to estimate the pose between consecutive frames, without any other sensors, such as Global Positioning System (GPS) and Inertial Measurement Unit (IMU). Firstly, the ground plane constraints were used to reduce matching errors. Then, based on more accurate lidar odometry obtained from lidar odometry and mapping (LOAM), SegMatch completed segmentation matching and loop detection to optimize the global pose. The neighborhood search was also used to accomplish the loop detection task in case of failure. Finally, the proposed method was evaluated and compared with the existing 3D lidar SLAM methods. Experiment results showed that the proposed method could realize low drift localization and dense 3D point cloud map construction.
Highlights
Over the last couple of decades, the application field of simultaneous localization and mapping (SLAM) has been paid more and more attention, especially in an intelligent vehicle
Motivated by the discussion above, we proposed optimized lidar odometry and mapping method using ground plane constraints and SegMatch-based loop detection, which achieved robust pose estimation and optimized global poses when detecting loop-closure
We proposed optimized lidar odometry and mapping method using ground plane
Summary
Over the last couple of decades, the application field of simultaneous localization and mapping (SLAM) has been paid more and more attention, especially in an intelligent vehicle. Compared with the visual sensor, the laser sensor has the advantages of high measurement accuracy, strong anti-interference ability, and wide sensing range, so the laser-based SLAM has higher positioning accuracy and better robustness. In the case of only using the lidar sensor, the pose change can only be calculated by matching between consecutive frames. In order to meet the real-time requirements, the pose estimation error obtained by the matching between frames and frames gradually increases when time changes, which is a typical problem in SLAM. Researchers have proposed loop detection algorithms to optimize global maps, thereby reducing drift errors. The laser point cloud has only position information and lacks color information, so the environmental information features are less, which brings greater challenges to the laser SLAM. Existing solutions for 3D closed-loop detection are computationally demanding
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