产生幻觉的机器人：从部分激光推断障碍物距离 测量

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摘要翻译：
许多移动机器人依靠二维激光扫描仪进行定位、测绘和导航。然而，这些传感器无法正确地提供与障碍物的距离，如玻璃面板和桌子，这些障碍物的实际占用率在传感器测量的高度是不可见的。在这项工作中，我们提出了一种直接从原始的2D激光数据估计距离的方法，而不是从更丰富的传感器读数如3D激光或RGBD传感器估计到障碍物的距离。为了学习从原始的2D激光距离到障碍物距离的映射，我们将问题框定为一个学习任务，并训练一个神经网络，形成一个自动编码器。针对当前任务提出了一种新的网络超参数配置，并在测试集上进行了定量验证。最后，我们在Care-O-Bot4上实时定性地证明了训练后的网络能够成功地从部分二维激光读数推断障碍物距离。
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英文标题：
《Hallucinating robots: Inferring Obstacle Distances from Partial Laser
  Measurements》
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作者：
Jens Lundell, Francesco Verdoja, Ville Kyrki
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最新提交年份：
2018
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分类信息：

一级分类：Computer Science        计算机科学
二级分类：Robotics        机器人学
分类描述：Roughly includes material in ACM Subject Class I.2.9.
大致包括ACM科目I.2.9类的材料。
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一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Signal Processing        信号处理
分类描述：Theory, algorithms, performance analysis and applications of signal and data analysis, including physical modeling, processing, detection and parameter estimation, learning, mining, retrieval, and information extraction. The term "signal" includes speech, audio, sonar, radar, geophysical, physiological, (bio-) medical, image, video, and multimodal natural and man-made signals, including communication signals and data. Topics of interest include: statistical signal processing, spectral estimation and system identification; filter design, adaptive filtering / stochastic learning; (compressive) sampling, sensing, and transform-domain methods including fast algorithms; signal processing for machine learning and machine learning for signal processing applications; in-network and graph signal processing; convex and nonconvex optimization methods for signal processing applications; radar, sonar, and sensor array beamforming and direction finding; communications signal processing; low power, multi-core and system-on-chip signal processing; sensing, communication, analysis and optimization for cyber-physical systems such as power grids and the Internet of Things.
信号和数据分析的理论、算法、性能分析和应用，包括物理建模、处理、检测和参数估计、学习、挖掘、检索和信息提取。“信号”一词包括语音、音频、声纳、雷达、地球物理、生理、（生物）医学、图像、视频和多模态自然和人为信号，包括通信信号和数据。感兴趣的主题包括：统计信号处理、谱估计和系统辨识；滤波器设计；自适应滤波/随机学习；（压缩）采样、传感和变换域方法，包括快速算法；用于机器学习的信号处理和用于信号处理应用的机器学习；网络与图形信号处理；信号处理中的凸和非凸优化方法；雷达、声纳和传感器阵列波束形成和测向；通信信号处理；低功耗、多核、片上系统信号处理；信息物理系统的传感、通信、分析和优化，如电网和物联网。
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一级分类：Statistics        统计学
二级分类：Machine Learning        机器学习
分类描述：Covers machine learning papers (supervised, unsupervised, semi-supervised learning, graphical models, reinforcement learning, bandits, high dimensional inference, etc.) with a statistical or theoretical grounding
覆盖机器学习论文（监督，无监督，半监督学习，图形模型，强化学习，强盗，高维推理等）与统计或理论基础
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英文摘要：
  Many mobile robots rely on 2D laser scanners for localization, mapping, and navigation. However, those sensors are unable to correctly provide distance to obstacles such as glass panels and tables whose actual occupancy is invisible at the height the sensor is measuring. In this work, instead of estimating the distance to obstacles from richer sensor readings such as 3D lasers or RGBD sensors, we present a method to estimate the distance directly from raw 2D laser data. To learn a mapping from raw 2D laser distances to obstacle distances we frame the problem as a learning task and train a neural network formed as an autoencoder. A novel configuration of network hyperparameters is proposed for the task at hand and is quantitatively validated on a test set. Finally, we qualitatively demonstrate in real time on a Care-O-bot 4 that the trained network can successfully infer obstacle distances from partial 2D laser readings.
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PDF链接：
https://arxiv.org/pdf/1805.12338

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