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3D激光雷达以其准确的3D点云数据、不受光照变化影响的特点，为高质量的定位与建图提供保障，具有重要的理论研究意义和广泛应用前景。本文针对3D激光雷达的定位与建图开展研究，论文的主要内容如下：
首先，介绍了3D激光雷达定位与建图的研究背景与意义。从3D激光雷达定位与建图、3D激光雷达与其它传感器融合的定位建图两方面进行现状综述，并对论文内容和结构进行了简介。
其次，针对3D激光雷达定位与建图方法中帧图匹配效率较低的问题，提出了一种基于有向几何点和稀疏帧的3D激光雷达定位与建图方法DGP-SLAM。一方面，有向几何点DGP特征在空间中分布稀疏，从而稠密的几何点特征地图可以被稀疏的DGP特征地图代替，提高了帧图匹配模块寻找数据关联的效率，同时基于DGP特征的严格数据关联策略保证了关联的准确性；另一方面，稀疏的融合帧进一步增加了地图的稀疏性，基于稀疏帧的DGP特征传播策略提高了地图特征的质量。此外，基于DGP特征的回环检测和位姿图优化提高了位姿估计的全局一致性。所提方法能够有效提高帧图匹配的效率，实验表明了所提方法的有效性。
第三，提出了一种基于帧图匹配和固定窗口平滑的分层式3D激光里程计HELO。深层的固定窗口平滑模块以浅层帧图匹配模块的位姿估计结果为输入，通过多帧位姿优化降低累积误差，并实时地输出高精度位姿估计。设计了以特征为中心的地图特征管理方式同时管理浅层和深层的特征，分别为浅层构建局部地图和为深层提供多帧数据关联。基于关联结果结合一对多测量残差策略提供逐特征的多帧位姿约束，同时引入方向残差提高位姿优化的精度。在数据集上的实验结果表明所提方法能够在保证实时性的前提下有效降低累积误差。
第四，提出了一种紧融合滤波和平滑的嵌套式3D激光里程计TFAS。滤波和平滑模块以嵌套窗口的方式紧密融合起来，二者共享窗口中的特征和位姿，一方面节省了内存消耗，另一方面，平滑模块的优化结果通过对滤波的反馈提高其定位精度。滤波中考虑了精修位姿优化以增强对外点数据关联的鲁棒性，还设计了多残差约束用以提高位姿优化结果，同时在平滑模块引入DGP特征三角化策略保证特征的准确性。所提方法的有效性在数据集上进行了实验验证。
第五，提出了一种基于有向几何点特征的3D激光-惯性里程计。稀疏的有向几何点特征保障了3D激光-惯性里程计的高效性，IMU测量在多状态迭代扩展卡尔曼滤波器框架下用于去除点云的旋转和平移畸变并预测状态，提高剧烈运动下的定位鲁棒性。设计了基于稳定局部地图的特征跟踪以及环境自适应的关键帧选择、剔除策略，提高了有向几何点特征在低线束3D激光雷达中方向估计的准确性，并减少了地图中关键帧的冗余。此外，还给出了自适应的激光-惯性初始化策略以实现不同状态下的系统初始化。在数据集上的实验结果验证了所提方法的鲁棒性。
最后，对本文工作进行了总结，并指出了需要进一步开展的研究工作。

With the characteristics of accurate 3D point cloud data and robustness to illumination change, 3D LiDAR provides the basis for high-quality localization and mapping. It is significant in both research and applications. This thesis concerns the research on 3D LiDAR localization and mapping. The main contents are as follows:
Firstly, the research background and its significance of this thesis are given. The research development of 3D LiDAR localization and mapping, and fused localization and mapping of 3D LiDAR with other sensors is reviewed. The content and structure of this thesis are introduced.
Secondly, aiming at the problem of low efficiency of scan-to-map matching in typical 3D LiDAR SLAM (simultaneous localization and mapping), a new method DGP-SLAM is proposed based on DGP (directed geometry point) and sparse frame. On one hand, DGP features are sparsely distributed in space, and the dense geometric point feature map is replaced by a sparse DGP feature map, which improves the efficiency of data association in the scan-to-map matching module. Besides, a strict data association strategy is designed to improve the accuracy of data association. On the other hand, sparse fusion frames further increase the sparsity of the map, and the DGP feature propagation strategy based on the sparse frame improves the quality of features in the map. Also, DGP feature-based loop detection and pose graph optimization are conducted for global consistency. The proposed method can effectively improve the efficiency of scan-to-map matching, and the experiments on the datasets verify the effectiveness of the proposed method.
Thirdly, a hierarchical 3D LiDAR odometry method HELO based on scan-to-map matching and fixed-lag smoothing is proposed. The high-level fixed-lag smoothing module takes the pose estimation result of the low-level scan-to-map matching module as its input. Through multi-frame pose optimization, the accumulated error is reduced and a high-precision pose is obtained in real time. Concretely, a feature-centric map feature management scheme is designed to manage features in both low-level and high-level modules, which builds the local map and provides multi-frame data associations for above modules, respectively. The results of data associations are then combined with the one-to-many measurement residual strategy to construct feature-wise multi-frame pose constraints, and orientation residuals are also introduced to improve the accuracy of pose optimization. The experimental results on datasets show that the proposed method can effectively reduce the accumulated error while ensuring real-time performance.
Fourthly, a nested 3D LiDAR odometry method TFAS with the tight fusion of filtering and smoothing is proposed. The filtering and smoothing modules are tightly integrated into two nested windows, and they share the features and poses in the windows. On one hand, the memory consumption is reduced, and on the other hand, the optimization result of the smoothing module provides feedback for the filtering module to improve its accuracy. In the filtering module, the strategy of the refined pose optimization is presented to enhance the robustness to outlier data association, and multi-residual constraint is designed to improve the pose optimization accuracy. Meanwhile, the DGP feature triangulation strategy is introduced in the smoothing module and the quality of features is improved. The effectiveness of the proposed method is experimentally verified on the datasets.
Fifthly, a LiDAR-inertial odometry based on the DGP feature is proposed. The sparse DGP features ensure the efficiency of the proposed odometry. The IMU measurement is used to remove the rotation and translation distortions of the point cloud and predict the state under the framework of multi-state iterative extended Kalman filter. As a result, the performance under severe motion is improved. The strategies of feature tracking based on the stable local map as well as the adaptive keyframe selection and culling are designed, which improve the direction estimation accuracy of DGP features in low-beam 3D LiDAR. Also, the redundant keyframes in the map are reduced. In addition, a LiDAR-inertial initialization strategy is presented to realize the system initialization with different motion states. The experimental results on the datasets verify the robustness of the proposed method.
Finally, the conclusions are given and future work is addressed.