CASIA OpenIR  > 毕业生  > 博士学位论文
基于3D激光雷达的定位研究
王诚鹏
2024-05
Pages136
Subtype博士
Abstract

       定位是机器人实现自主作业的重要前提。3D激光雷达具有距离测量准、不受光照变化影响等优点,为机器人高质量定位提供保障,因此激光雷达定位技术的研究具有重要理论意义和广泛应用前景。本文针对基于3D激光雷达的定位开展研究,论文的主要内容如下:
       首先,介绍了3D激光雷达定位的研究背景和意义。从3D激光雷达定位、3D激光-惯性定位以及3D激光雷达位置识别三个方面进行现状综述,并对论文内容和结构安排进行了简介。
       其次,针对基于分布的3D激光里程计精度和效率的矛盾,提出了一种基于紧关联分布和最大似然估计的激光里程计HDLO。设计源分布到目标分布的稀疏数据关联,并对每一对成功关联的分布,利用相应点集的并集表示参考分布,从而通过分布紧关联的处理,改善局部结构的表征。在此基础上,基于最大似然估计构建以源分布点到参考分布距离为约束的代价函数,并设计了计算解耦策略,通过分布参数的预计算使得优化复杂度与点的数量无关,在满足实时性同时获得准确的位姿估计。同时,将上述数据关联和优化方案泛化到多帧,构建基于分布的帧间交叉约束,进而联合优化固定窗口内多帧位姿,以降低累积误差,改善位姿估计的局部一致性。HDLO在KITTI数据集上的平均相对平移和平均相对旋转误差分别为0.50%和0.16度/100米,表明了所提方法的有效性。
       第三,将IMU引入上述激光里程计HDLO中,提出了一种基于分布的分层紧耦合3D激光-惯性里程计HD-LIO,以提高定位的鲁棒性和精度。为了解决IMU和激光雷达紧耦合过程中点云分布约束的退化问题,基于点云测量噪声在点到分布距离观测方程中的传播,设计了一个随分布参数动态变化的核函数,通过对损失项的调整获得抗退化的点云分布约束,保证了剧烈运动条件下位姿估计的稳定性。在此基础上,滤波和平滑以分层紧耦合的方式实现定位,其中,低层基于迭代扩展卡尔曼滤波器,构建IMU先验和抗退化点云分布联合约束,实时估计当前帧的位姿,高层则联合先验约束、IMU预积分、点云观测约束对多帧位姿进行固定窗口平滑,在保证效率的同时,获得由粗到精且鲁棒的定位结果。HD-LIO在NC和ENC数据集上的平均相对平移和平均相对旋转误差分别为0.49%和1.97度/100米,证明了所提方法的有效性。
       第四,提出了一种基于距离图和循环列移不变注意力的激光雷达位置识别方法CSINet。现有基于距离图的方法依赖于NetVLAD的不变性实现对点云旋转的鲁棒性,限制了特征提取过程中的尺度变化。针对该问题,设计了循环列移不变的注意力,通过对输入特征的平均池化和最大池化结果进行加权并循环卷积,生成与输入等变的注意力权重,以此基于矩阵相乘方式对输入进行加权,在捕获全局上下文信息的同时,实现输出对距离图输入沿列方向循环移动的不变性,这为可变的特征尺度提供了保障。基于此,设计空间降采样和空间通道混合的多尺度特征增强模块,以挖掘注意力输出特征中不同尺度的信息,提高全局特征向量的辨别性,促进位置识别性能。在NCLT、KITTI和Ford数据集上的实验结果验证了所提方法的有效性。
       第五,以上述3D激光-惯性里程计HD-LIO作为前端,并集成所提激光雷达位置识别方法CSINet与位姿图构成的闭环优化后端,设计了基于自适应分布的3D激光-惯性定位与建图框架ADLI-SLAM。对于前端里程计,其低层滤波加入了场景自适应的分布提取策略,以确保获得充足的分布约束,提高楼梯等挑战环境下的定位鲁棒性;高层平滑则额外考虑了点云分布参数,通过最小化体素中分布协方差矩阵的行列式实现位姿与分布的捆绑优化,进一步改善估计准确性。后端主要利用位置识别方法CSINet检测固定窗口平滑中最新点云帧的候选闭环帧,并通过一致性检验生成闭环因子,进而结合平滑优化产生的里程计因子进行基于全局位姿图的闭环优化,提高定位与建图的全局一致性。所提方法的性能在公开数据集上进行了验证。
       最后,对本文工作进行了总结,并指出了需要进一步开展的工作。

Other Abstract

    Localization is a crucial prerequisite for autonomous operations of robots. With the advantages of accurate distance measurement and robustness to illumination change, 3D LiDAR (Light Detection and Ranging) provides the basis for high-quality localization of robots. Therefore, the research on 3D LiDAR localization is significant in theory with widespread applications. This thesis conducts the research on localization based on 3D LiDAR. The main contents are as follows:
    Firstly, the research background and significance of this thesis are given. The research developments of 3D LiDAR localization, 3D LiDAR-inertial localization, and 3D LiDAR place recognition are reviewed. The content and structure of this thesis are introduced.
    Secondly, aiming at the contradiction between the accuracy and efficiency in distribution-based 3D LiDAR odometry, a new method HDLO based on tightly associated distribution and maximum likelihood estimation (MLE) is proposed. A sparse data association from source distribution to target distribution is designed. For each pair of associated distributions, the union of corresponding point sets is utilized to represent the reference distribution, which associates the matched distributions tightly and thereby improves the representation of local structures. On this basis, a cost function that takes the point-to-reference distribution distances as constraints is constructed using MLE, and a computational decoupling strategy is presented. Through the pre-calculation of distribution parameters, the optimization complexity is decoupled from the number of points. As a result, the accurate pose and real-time performance are both obtained. Meanwhile, the aforementioned data association and optimization scheme is generalized to the situation of multiple frames. With the distribution-based inter-frame cross constraints, multi-frame poses within the fixed-size window are jointly optimized. The cumulative error is then reduced and the local consistency of pose estimation is improved. The average relative translation and average relative rotation errors of HDLO on the KITTI dataset are 0.50% and 0.16 degrees/100 meters, respectively. This demonstrates the effectiveness of the proposed method.
    Thirdly, a hierarchical tightly-coupled 3D LiDAR-inertial odometry based on distribution is proposed by introducing IMU into the aforementioned HDLO to improve the robustness and accuracy of localization, which is termed as HD-LIO. To solve the degradation problem of point cloud distribution constraints in the tight coupling process of IMU and LiDAR, a loss function that dynamically changes with the distribution parameters is designed according to the propagation of point cloud measurement noise in the point-to-distribution distance observation equation. By adjusting the loss term, the anti-degradation point cloud distribution constraints are generated, ensuring the stability of pose estimation under the aggressive motions. On this basis, filtering and smoothing are integrated to achieve localization in a hierarchical tightly-coupled manner. With the iterative extended Kalman filter, the low level constructs joint constraints of IMU prior and anti-degradation point cloud distribution to estimate the pose of the current frame in real time. The high level combines prior, IMU pre-integration, and point cloud observation constraints to perform fixed-lag smoothing on multi-frame poses. In this way, the efficiency is ensured and a coarse-to-fine odometry estimation with robustness is realized. The effectiveness of the proposed method is verified on the datasets. The average relative translation and average relative rotation errors of HD-LIO on the NC and ENC datasets are 0.49% and 1.97 degrees/100 meters, respectively, which proves the effectiveness of the proposed method.
    Fourthly, a LiDAR place recognition method CSINet is proposed, which is based on range image and cyclic column-shift-invariant attention. The existing range image-based solutions resort to the invariance of NetVLAD for the robustness to point cloud rotation, which restricts the change of scales during feature extraction. To address this problem, a cyclic column-shift-invariant (CSI) attention is designed. It weights outputs of average pooling and maximum pooling on the input feature, which is then cyclically convoluted to generate attention map equivariant to the input. By weighting the input feature based on matrix multiplication, the invariance to the cyclic shift of range image along the column direction is achieved while capturing global contextual information, which provides a prerequisite for the variation of feature scales. Then, a multi-scale feature enhancement module based on spatial downsampling and spatial-channel mixing is presented to mine the information of different scales on the output features of CSI attention. This enhances the discriminability of the global feature vector and facilitates the place recognition performance. The experiment results on the NCLT, KITTI, and Ford datasets verify the effectiveness of the proposed method.

    Fifthly, a 3D LiDAR-inertial localization and mapping framework ADLI-SLAM based on adaptive distribution is designed, which takes the aforementioned 3D LiDAR-inertial odometry HDLO as the frontend and combines the loop closure optimization backend consisting of the proposed LiDAR place recognition method CSINet and pose graph. For the frontend, a scene-adaptive distribution extraction strategy is added at its low-level filtering to guarantee sufficient distribution constraints and reinforce localization robustness in challenging environments such as stairs. Also, its high-level smoothing additionally considers point cloud distribution parameters, and thus implements the bundle optimization of pose and distribution by minimizing the determinant of the distribution covariance matrix in voxels. It further improves the estimation precision. The backend applies the place recognition method CSINet to detect the candidate loop closure frame corresponding to the latest point cloud in the fixed-lag smoothing, and generates loop closure factor after the consistent check. Further, the loop closure factor is combined with odometry factors from the smoothing to execute loop closure optimization within the global pose graph, which improves the global consistency of localization and mapping. The performance of the proposed method is verified on public datasets.
    Finally, the conclusions are given and future work is presented.

Keyword3D激光雷达定位 紧关联分布 里程计 激光-惯性融合 位置识别
Subject Area机器人控制
MOST Discipline Catalogue工学 ; 工学::控制科学与工程
Language中文
IS Representative Paper
Sub direction classification智能机器人
planning direction of the national heavy laboratory其他
Paper associated data
Document Type学位论文
Identifierhttp://ir.ia.ac.cn/handle/173211/57371
Collection毕业生_博士学位论文
Recommended Citation
GB/T 7714
王诚鹏. 基于3D激光雷达的定位研究[D],2024.
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