面向复杂场景自主导航的主动场景流估计技术研究

       自主导航系统包括全局规划、环境感知、局部规划三个主要模块，输入全局目标、感知信号以及导航地图，输出自主车辆运行的控制命令。全局规划模块用于产生粗粒度的运动路径；环境感知模块用于估计实时自车位姿、物体运动信息以及可行驶区域；局部规划模块用于计算自车的位姿和合理的细粒度的运动轨迹，并基于这些计算生成相应的运动控制命令。与其他感知技术相比，场景流估计技术可以计算出场景中每个三维点的运动信息，有助于实时精确地定位自车位置和检测运动目标，准确估算可行驶区域与其他物体的运动轨迹，生成合理的运动控制命令，提高系统安全和决策效率。然而，导致点云数据中的对应点缺失的复杂环境和点云降采样操作，增加了场景流估计的误差。同时，场景流估计中不当的上采样操作也会增加估计误差。目前场景流估计技术面临的挑战包括复杂环境中数据的对应点缺失和算法对时空相关性及点云数据质量考虑不足。因此，为提高场景流估计准确性，需考虑这些因素并改进算法。
       主动场景流估计技术通过实时主动选取合理的自车点云观测位姿作为局部目标，利用运动规划算法，提高观测数据质量，减少相邻点云帧之间的对应点缺失，结合场景流估计算法提高实时估计的准确度。然而，主动场景流估计技术的研究面临以下挑战：(1) 缺乏支持该研究的数据集与算法训练验证的一体化技术架构；(2) 如何在大尺度环境下优化场景流估计方法；(3) 如何在小尺度环境下优化场景流估计方法；(4) 如何通过智能的局部目标选取与自车运动规划来提高点云数据采集质量。为解决上述问题，主动场景流估计技术需要从点云数据主动观测和场景流实时估计两个角度进行改进。本文针对主动场景流估计所需要的数据集与算法验证架构、大尺度场景流估计算法设计、小尺度场景流估计算法设计、以及点云数据主动观测开展了以下四个方面的研究：
       第一，提出了一套适应性高、面向场景流估计算法的训练与验证、基于自动驾驶仿真器的主动场景流数据集构建与标注技术的一体化架构。首先，该架构通过自定义交通场景实现了多样性数据的自动采集，满足了主动场景流估计算法进行训练和验证的需求；其次，该架构能够解决点云主动观测技术无法进行交互验证的问题，满足了主动观测方法对可重现场景进行实时数据采集与算法验证的要求；最后，该架构基于物体标签一致性的自动标注方法，提供了准确的数据真值，以便于算法评估。实验结果表明：本研究所提出的场景流数据集构建与验证框架能满足对数据多样性、标注准确性以及估计算法验证的需求。
       第二，提出了一种基于时空特征一致性的场景流估计方法，以解决大尺度复杂场景中对应点缺失、运动模式复杂以及运动尺度大对场景流估计的影响。首先，通过点云空间变换，基于交叉注意力机制捕获点云在时间维度的特征一致性，结合点云的上下文信息提高点云特征双向匹配的准确性。其次，通过多尺度场景流空间维度的特征一致性来提取点云中各点之间的空间关系，确保了场景流估计特征的稳健性。最后，通过时空一致性的双向注意力模块的神经网络以及Transformer注意力机制的并行计算能力，优化了场景流估计的精度，节约了计算时间。实验结果表明：所提出的方法不仅能够解决大尺度场景流估计中影响估计准确度的问题，还能降低计算时间。
       第三，提出了一种基于局部特征一致性的场景流估计方法，以解决小尺度场景中的对应点缺失、忽略上采样错误，以及大尺度场景流估计对局部微小运动不敏感的问题。首先，通过引入MLP-Mixer操作和多尺度双向对称特征相似度匹配代价估计的策略，减少了由于对应点缺失引起的点云特征关联问题。其次，通过融合场景流特征的语义和几何信息，以及考虑相邻帧和同一帧数据之间的局部邻域特征相关性，提高场景流上采样质量。最后，通过交替更新特征匹配代价估计和场景流估计的网络结构，整体提高场景流估计的准确度。实验结果表明：所提出的方法不仅能够改善场景流估计准确度和场景流特征质量，还能提高上采样场景流的准确度。
       第四，提出了一种基于局部目标主动选取与智能运动规划的主动观测方法，以解决较差质量的点云对场景流估计的影响。首先，通过未来时刻点云预测和隐藏点去除，结合道路信息，建立自车位姿的合法性检查方案，设计可靠的可行驶区域估计方法。其次，通过设计合理的场景相似性度量指标，全面评估两帧点云之间的差异，选取最优的局部目标。最后，通过自适应的感知置信度网络和奖励函数，提出一种基于强化学习的运动规划方法，保障自车通过可行驶区域安全到达局部目标。实验结果表明：所提出的局部目标主动选取技术能够有效地改善点云数据质量、提升场景流估计算法性能；所提出的运动规划方法具有较高的避障成功率，确保了自主导航系统能够安全到达局部目标。
       综上所述，本研究围绕数据集构建与估计算法验证架构、场景流估计算法设计、点云主动观测技术展开，确保了算法的理论可行性和实际可靠性。

   The autonomous navigation systems usually consists of three modules: global planning, environment perception, and local planning. The global planning module is dedicated to formulating the coarse-grained motion path for the autonomous vehicle. The environment perception module can estimate the ego-vehicle's position in real-time, motion information about surrounding objects, and drivable areas for itself. Meanwhile, the local planning module focuses on selecting appropriate local waypoints, refining the local motion path, and generating corresponding motion control commands. In contrast to other environment perception methods, the scene flow (SF) estimation technique can estimate the 3D point-wise motion vector based on the point cloud measurement data from the environment. This technique facilitates precise ego-vehicle localization, real-time detection of moving targets, accurate estimation of drivable areas and the ego-vehicle's motion trajectory, outputting reasonable motion control commands, and enhancing system safety and decision-making efficiency. However, when confronted with hazard factors such as object obstructions, rapid-moving entities, and the down-sampling of point cloud data within perception methods, many corresponding points between successive point cloud frames may be lost, leading to errors in SF estimation. Meanwhile, improper upsampling operations in SF estimation can also cause estimation errors.
    Many existing methodologies inadequately exploit the spatiotemporal correlations inherent in point clouds, thereby neglecting the impact of data quality on the precision of SF estimation. Consequently, improving existing methods while considering these factors is imperative to improve the accuracy and robustness of SF estimation for complex traffic environments. The active SF estimation method can enhance the quality of the point cloud and reduces the occurrence of missing corresponding points between adjacent frames of point cloud data, which is achieved by judiciously selecting reasonable ego-vehicle poses as local targets and employing motion planning methods to improve the accuracy of real-time SF estimation. However, developing a robust active SF estimation method in dynamic environments has to address the following technical challenges: (1) how to build an active SF dataset for verification, (2) how to optimize real-time estimation of the SF within a large-scale view, (3) how to optimize real-time estimation of the SF within a limited view, and (4) how to enhance SF quality through active sensing techniques.
    To address above issues, the active SF estimation technique must be improved from two perspectives: active observation of point cloud data and real-time SF estimation. This thesis undertakes the research in four key areas related to active SF estimation: (1) the framework for dataset construction and method validation, (2) the small-scale SF estimation method, (3) the large-scale SF estimation method, and (4) active observation techniques for improving point cloud data quality.
    First, we propose an integrated framework for constructing and annotating active SF datasets, tailored for algorithm training and validation in autonomous driving system simulations. At first, this framework utilizes self-defined traffic scenarios to autonomously collect data at any scale, to meet the requirements of validating active SF estimation algorithms. Then, this framework addresses the problem of lacking interactive validation of point cloud observation techniques, to meet the requirements of active observation methods for data collection and algorithm verification of reproducible scenes. Furthermore, it employs instance-level object-matching methods to automatically annotate SF data, providing accurate ground truth for algorithm evaluation. Experimental results demonstrate that the proposed method for constructing and annotating SF datasets satisfies the demands for data diversity, annotation precision, and active SF estimation methods validation.
   Second, we present an approach to SF estimation in large-scale complex environments, aiming to address the challenges posed by the absence of corresponding points, complicated motion patterns, and large motion scales. At first, we develop a temporal feature bi-directional matching based accuracy improvement strategy, achieved by using point cloud spatial transformation, a self-attention mechanism, and contextual point cloud information. Then, we develop a spatial multi-scale SF feature consistency to extract the spatial correspondence points, enhancing the stability of SF estimation features. Finally, we develop a neural network with a spatiotemporal consistency bi-directional attention mechanism module to reduce the missing points'harmful impacts and refine SF estimation accuracy, featured with the parallel computing capability of the Transformer attention mechanism. Experimental results validate the efficacy of our method, which not only addresses challenges stemming from data noise and missing data in large-scale SF estimation but also effectively distinguishes complex scene features.
   Third, we propose a local feature consistency-based SF estimation method to address the harmful impacts of missing correspondence points and up-sampling operations on SF estimation in small-scale complex scenes. At first, we develop a robust multi-scale bi-directional symmetric feature similarity strategy and the MLP-Mixer operation to address the point cloud feature association problem caused by missing correspondence points. Then, we develop an upsampling strategy by fusing the semantic and geometric information of scene stream features and considering the local neighborhood feature correlation between neighboring frames and data from the same frame, contributing to improved SF up-sampling quality. Finally, we develop a network structure featuring two alternate modules of updated cost volume and SF estimation for improving the SF estimation accuracy. Experimental results demonstrate that the proposed method can improve the SF estimation accuracy and SF feature quality, as well as enhance the fidelity of the up-sampled SF.
    Fourth, we propose an active observation method based on intelligent local target selection and reinforcement learning based motion planning to mitigate the impact of low-quality point clouds on SF estimation. At first, we develop a reliable reachable area estimation strategy through hidden points removal based scene prediction and a legality checking scheme between the ego-vehicle and the road as well as between the ego-vehicle and the predicted scene. Then, we develop an efficient local target decision strategy by comprehensively evaluating the disparity between two frames of point clouds using a suitable scene similarity measure. Finally, we develop a reinforcement learning-based motion planning approach with an adaptive perception confidence network and reward function to ensure the safe arrival at the local target through the drivable area for the ego-vehicle. Experimental results demonstrate that the proposed active local target selection technique effectively enhances point cloud data quality and improves SF estimation algorithm performance. Additionally, the motion planning method yields a high success rate in obstacle avoidance, ensuring the secure arrival at local targets.
    In summary, this thesis revolves around four main topics: the development of an active SF dataset and estimation method validation framework, the development of a point cloud active observation strategy, and the development of SF estimation techniques applicable to both large-scale and small-scale scenes. These efforts aim to ensure the theoretical soundness and practical applications of the proposed methodology.

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