Improving the Perception of Autonomous Robotic Systems through Sensing and Machine Learning

最近，机器人领域在科学成就和工业应用方面都经历了戏剧性的激增。已经提出了许多任务的自动化，例如驾驶、对象操纵或仓库操作。为了缓解与不确定性或不断变化的条件相关的困难，有必要改进感知管道。在这项提议中，我们将在情报层面和感知层面上改进它。对于前者，我们将探索两种机器学习技术的使用：域适应(DA)和稀疏编码(SC)。域自适应旨在提高在数据集上训练的分类器的性能，但用于分布略有不同的数据。稀疏编码试图通过寻找潜在较长特征向量的几个分量是活动的(非零)的表示来自动化特征提取问题。例如，在传感层面，我们建议探索使用定制的高光谱相机。*对于我们的短期目标，我们已经确定了机器人领域的三个关键问题，我们希望为此做出重大贡献：视觉位置识别、森林中的自主导航和抓取自动化。在地点识别中使用DA将改善对在不同光照或天气条件下拍摄的同一位置图像的检测。为了消除图像中的阴影，高光谱相机可以产生更好的颜色恒定的图像。对于森林环境，我们将同时研究使用这种高光谱相机和3D激光雷达扫描进行地点识别。此外，我们将对用于森林导航的地形图的创建过程提出改进建议。对于抓取，我们提出了一种更丰富的抓取位置的表示形式，称为半圆柱视图。我们还将通过融合多个视图来增加3D传感的健壮性。最后，我们将使用稀疏编码进行表示学习。*实验将在真实数据或机器人上进行。例如，我们将在四季中定期收集魁北克市的数据集，以测试我们的视觉地点识别方法。对于森林导航，我们将在拉瓦尔大学校园的森林中使用我们的ClearPath Robotics赫斯基A200机器人及其传感器套件。对于抓取，我们将用真实的机械臂测试我们的算法。*我们希望通过先进机器学习方法或传感方法的新应用，为机器人学做出重大的科学贡献。例如，我们认为没有人在地点识别的背景下探索过领域适应的范式。我们还预计，我们的研究成果将直接转移到行业中。最后，我们将培训3名本科生、2名硕士和4名博士，他们的技能和知识将使加拿大工业受益。

Recently, the field of robotics has experienced a dramatic surge, both in terms of scientific accomplishments and industrial applications. The automation of numerous tasks has been proposed, such as driving, object manipulation or warehouse operation. To mitigate the difficulties associated with uncertainties or changing conditions, improvements to the perception pipeline are necessary. In this proposal, we will improve it both at the intelligence level and at the sensing level. For the former, we will explore the use of two machine-learning techniques: Domain Adaptation (DA) and Sparse Coding (SC). Domain Adaptation aims at improving performance of a classifier trained on a data set but used on data which is distributed slightly differently. Sparse Coding tries to automate the problem of feature extraction, by finding representations where few components of a potentially long feature vector are active (non-zero). At the sensing level, we propose for example to explore the use of custom-made hyperspectral cameras.******For our short term objectives, we have identified 3 key problems in robotics for which we seek to make significant contributions: visual place recognition, autonomous navigation in forests, and grasping automation. The use of DA in place recognition will improve the detection of images of the same location taken under different illumination or weather conditions. In order to remove shadows in images, better color-constant images can be generated from hyperspectral cameras. For forested environments, we will study, in parallel, the use of this hyperspectral camera and of 3D LiDAR scans for place recognition. On top of that, we will propose ameliorations to the creation process of topometric maps, used for forest navigation purposes. For grasping, we propose a richer representation of a grasping location, called hemicylindrical view. We will also increase the robustness of the 3D sensing by fusing multiple views. Finally, we will perform representation learning with Sparse Coding. ******Experiments will be conducted on real data or robots. For instance, we will regularly gather datasets in Quebec City, over the four seasons to test our visual place recognition methods. For forest navigation, we will use our Clearpath Robotics Husky A200 robot and its sensor suite in the forests located on Laval University campus. For grasping, we will test our algorithms with real robotic arms.*******We expect to make significant scientific contributions to robotics, in the form of novel applications of advanced machine learning methods or sensing approaches. For example, we do not believe that anyone has explored the paradigm of Domain Adaptation in the context of place recognition. We also expect that our research results will transfer directly to the industry. Finally, we will train 3 Undergraduates, 2 Masters and 4 PhDs with skills and knowledge that will benefit the Canadian industry.***