论文标题
用于扩展和增强视觉大满贯的在线语义映射系统
An Online Semantic Mapping System for Extending and Enhancing Visual SLAM
论文作者
论文摘要
我们为移动视觉系统提供了一种实时的语义映射方法，该方法具有2D至3D对象检测管道和生成地标的快速数据关联。除了语义图富集以外，相关的检测进一步引入了语义约束，以同时定位和映射（SLAM）系统以进行姿势校正。这样，我们能够生成其他有意义的信息，以实现更高级别的任务，同时利用对象检测的观察不变，以提高探光度估计的准确性和鲁棒性。我们提出了本地关联的对象观测值的曲目，以处理歧义和错误的预测以及基于不确定性的贪婪关联方案，以加速处理时间。我们的系统达到平均迭代持续时间为65〜MS的实时功能，并且能够在公共数据集中提高最先进的SLAM的姿势估计高达68％。此外，我们将方法实现为模块化ROS软件包，使其直接在基于图形的SLAM方法中集成。
We present a real-time semantic mapping approach for mobile vision systems with a 2D to 3D object detection pipeline and rapid data association for generated landmarks. Besides the semantic map enrichment the associated detections are further introduced as semantic constraints into a simultaneous localization and mapping (SLAM) system for pose correction purposes. This way, we are able generate additional meaningful information that allows to achieve higher-level tasks, while simultaneously leveraging the view-invariance of object detections to improve the accuracy and the robustness of the odometry estimation. We propose tracklets of locally associated object observations to handle ambiguous and false predictions and an uncertainty-based greedy association scheme for an accelerated processing time. Our system reaches real-time capabilities with an average iteration duration of 65~ms and is able to improve the pose estimation of a state-of-the-art SLAM by up to 68% on a public dataset. Additionally, we implemented our approach as a modular ROS package that makes it straightforward for integration in arbitrary graph-based SLAM methods.
