本文为法国格勒诺布尔综合理工学院（作者：Trung-Dung Vu）的博士论文，共127页。

感知或理解车辆周围环境是建立驾驶辅助系统或自主车辆的一个非常重要的步骤。本文以激光扫描仪为主要感知传感器，研究动态室外环境下运动目标检测、分类和跟踪的同步定位与映射问题。人们相信，如果能够可靠地实时完成这些任务，这将打开一个广阔的潜在汽车应用领域。

本文的第一个贡献是基于网格的方法来解决运动目标检测中的SLAM问题。为了从里程计中校正车辆位置，我们提出了一种新的快速增量扫描匹配方法，该方法在动态室外环境下工作可靠。在估计出良好的车辆位置后，对周围的地图进行增量更新，在没有目标先验知识的情况下检测出运动目标。实验结果表明，该方法是有效的。

第二个贡献是在获得良好的车辆定位和可靠的地图后，是基于第一个贡献的。本文以运动目标为研究对象，提出了一种同时检测、分类和跟踪运动目标的方法。提出了一种基于模型的方法，通过对运动目标轨迹的假设来解释滑动时间窗上的激光测量序列。采用数据驱动的马尔可夫链蒙特卡罗（DDMCMC）技术解决时空数据关联问题，有效地找到最有可能的解。在实际的城市交通数据上进行了实验，取得了良好的效果。

第三个贡献是将我们的感知模块集成到一个真实的车辆上，用于一个特定的安全汽车应用程序，称为预碰撞Pre-Crash。这项工作是在欧洲项目PReVENTProFusion1的框架内与戴姆勒Daimler合作完成的。基于相关的碰撞和非碰撞场景，给出了一个综合的实验评估，验证了该方法的稳健性和可靠性。

Perceiving or understanding the environment surrounding of a vehicle is a very important step in building driving assistant systems or autonomous vehicles. In this thesis, we study problems of simultaneous localization and mapping (SLAM) with detection, classification and tracking moving objects in context of dynamic outdoor environments focusing on using laser scanner as a main perception sensor. It is believed that if one is able to accomplish these tasks reliably in real time, this will open a vast range of potential automotive applications. The first contribution of this research is made by a grid-based approach to solve both problems of SLAM with detection of moving objects. To correct vehicle location from odometry we introduce a new fast incremental scan matching method that works reliably in dynamic outdoor environments. After good vehicle location is estimated, the surrounding map is updated incrementally and moving objects are detected without a priori knowledge of the targets. Experimental results on datasets collected from different scenarios demonstrate the efficiency of the method. The second contribution follows the first result after a good vehicle localization and a reliable map are obtained. We now focus on moving objects and present a method of simultaneous detection, classification and tracking moving objects. A model-based approach is introduced to interpret the laser measurement sequence over a sliding window of time by hypotheses of moving object trajectories. The data-driven Markov chain Monte Carlo (DDMCMC) technique is used to solve the data association in the spatio-temporal space to effectively find the most likely solution. We test the proposed algorithm on real-life data of urban traffic and present promising results. The third contribution is an integration of our perception module on a real vehicle for a particular safety automotive application, named Pre-Crash. This work has been performed in the framework of the European Project PReVENTProFusion1 in collaboration with Daimler. A comprehensive experimental evaluation based on relevant crash and non-crash scenarios is presented which confirms the robustness and reliability of our proposed method.

1. 引言

2. 车辆感知最新进展

3. 检测运动目标的基于网格的SLAM

4. 基于马尔科夫链蒙特卡洛的DATMO

5. 实际应用：PreCrash

6. 结论与展望

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