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Semantic mapping and semantics-boosted navigation with path creation on a mobile robot

机译：语义映射和语义 - 移动机器人的路径创​​建导航

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摘要

Movable obstacles are challenges for robotic navigation in human living environments because they lead to frequent changes in pre-built maps. Such changes cause navigation failures and high costs in path replanning. In this paper, we focus on the problem of semantic mapping and semantics-boosted navigation, introducing our intelligent robotic system that is capable of real-time belief-based scene understanding, object detection and recognition, as well as path creation strategy based on the semantic information. With deeper understanding of the environments, robotic navigation in cluttered environments filled with movable obstacles is improved. Scene understanding and object detection are achieved by our state-of-the-art Convolu-tional Neural Network (CNN) with multi-functions and then transformed into robotic beliefs using Bayes filtering to ensure temporal coherence. Object beliefs are further extended from 2D to 3D space with geometric feature detection and used for semantic mapping together with scene beliefs. Planning algorithm is designed to utilize semantic beliefs to create path through cluttered environments by manipulating movable obstacles at minimal costs. We evaluate the system on our robots with real human-living office scenarios.
机译：可移动障碍是人类生活环境中机器人导航的挑战，因为它们导致预先构建地图的频繁变化。此类变化导致导航失败和高成本在路径重新恢复中。在本文中，我们专注于语义映射和语义提升导航的问题，介绍了我们能够实时信念的现场了解，对象检测和识别的智能机器人系统，以及基于的路径创建策略语义信息。随着对环境的更深入了解，凌乱的环境中充满可移动障碍物的杂乱环境中的机器人导航。现场了解和对象检测由我们的最先进的卷积神经网络（CNN）实现了多功能，然后使用Bayes滤波转换为机器人信念，以确保时间相干。物体信仰进一步从2D到3D空间延伸到带有几何特征检测，并用于与场景信仰一起的语义映射。规划算法旨在利用语义信仰来通过以最小成本操纵可移动障碍来创造通过杂乱环境的路径。我们以真正的人类生活办公场景评估了我们的机器人的系统。

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