论文标题
Slamer：同时本地化和地图辅助环境识别
SLAMER: Simultaneous Localization and Map-Assisted Environment Recognition
论文作者
论文摘要
本文提出了同时定位和地图辅助环境识别方法（SLAMER）方法。移动机器人通常具有环境图，并且可以将环境信息分配给地图。如果本地化成功，则可以预测移动机器人的重要信息，因为它们的相对姿势可以知道。但是，当本地化不起作用时，该预测失败了。必须使用地图信息来考虑姿势估计的不确定性。此外，机器人具有外部传感器，并且可以使用传感器识别环境信息。当然，这种在线认可包含不确定性。但是，它必须与MAP信息融合以进行健壮的环境识别，因为随着时间的流逝，该地图还包含不确定性。 Slamer可以同时应对这些不确定性，并实现准确的本地化和环境识别。在本文中，我们在两种情况下演示了基于激光雷达的Slamer的实施。在第一种情况下，我们使用Semantickitti数据集，并表明Slamer比传统方法更能实现准确的估计。在第二种情况下，我们使用室内移动机器人，并证明无法衡量的环境对象，例如打开门和无法识别入口行。
This paper presents a simultaneous localization and map-assisted environment recognition (SLAMER) method. Mobile robots usually have an environment map and environment information can be assigned to the map. Important information for mobile robots such as no entry zone can be predicted if localization has succeeded since relative pose of them can be known. However, this prediction is failed when localization does not work. Uncertainty of pose estimate must be considered for robustly using the map information. In addition, robots have external sensors and environment information can be recognized using the sensors. This on-line recognition of course contains uncertainty; however, it has to be fused with the map information for robust environment recognition since the map also contains uncertainty owing to over time. SLAMER can simultaneously cope with these uncertainties and achieves accurate localization and environment recognition. In this paper, we demonstrate LiDAR-based implementation of SLAMER in two cases. In the first case, we use the SemanticKITTI dataset and show that SLAMER achieves accurate estimate more than traditional methods. In the second case, we use an indoor mobile robot and show that unmeasurable environmental objects such as open doors and no entry lines can be recognized.
