Localisation using the appearance of prior structure
Localisation using the appearance of prior structure
复制标题
使用先前结构的外观进行定位
DOI：
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复制
发表时间：
2014
期刊：
影响因子：
--
通讯作者：
P. Newman
中科院分区：
文献类型：
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作者：
P. Newman
精确和鲁棒的定位是任何自主移动的机器人的基本方面。然而，如果这些要普及，还必须以低成本提供。在这篇论文中，我们开发了一种新的方法来定位使用单目摄像机，利用彩色3D点云之前的环境，以前捕获的调查车辆。我们不做任何假设的外部条件，在机器人的遍历相对于所经历的调查车辆，我们也不做任何假设，他们的相对传感器配置。我们的方法不使用提取的图像特征。相反，它明确地优化姿态，这在香农意义上协调了关于来自以姿态为条件的捕获图像的场景的外观的信息与先验的信息。我们使用作为我们的目标的归一化信息距离（NID），一个真正的度量信息，并证明作为结果的鲁棒性，我们的本地化配方照明变化，遮挡和色彩空间变换。我们提出了如何，通过建设的联合分布的场景的外观从先验和现场图像，可以计算的NID的梯度，以及如何这些可以用来有效地解决我们的配方使用拟牛顿方法。为了可靠地识别任何本地化失败，我们提出了一个新的分类器，使用的NID的局部形状的候选人的姿势，并展示了完整的系统从其使用的性能增益。最后，我们详细介绍了我们的方法使用商品GPU的实时实现的发展，并证明它优于一个高档的，商业GPS辅助INS在牛津市中心的57公里的驾驶，在一系列不同的条件下，一天和一年的时间。
Accurate and robust localisation is a fundamental aspect of any autonomous mobile robot. However, if these are to become widespread, it must also be available at low-cost. In this thesis, we develop a new approach to localisation using monocular cameras by leveraging a coloured 3D pointcloud prior of the environment, captured previously by a survey vehicle. We make no assumptions about the external conditions during the robot’s traversal relative to those experienced by the survey vehicle, nor do we make any assumptions about their relative sensor configurations. Our method uses no extracted image features. Instead, it explicitly optimises for the pose which harmonises the information, in a Shannon sense, about the appearance of the scene from the captured images conditioned on the pose, with that of the prior. We use as our objective the Normalised Information Distance (NID), a true metric for information, and demonstrate as a consequence the robustness of our localisation formulation to illumination changes, occlusions and colourspace transformations. We present how, by construction of the joint distribution of the appearance of the scene from the prior and the live imagery, the gradients of the NID can be computed and how these can be used to efficiently solve our formulation using Quasi-Newton methods. In order to reliably identify any localisation failures, we present a new classifier using the local shape of the NID about the candidate pose and demonstrate the performance gains of the complete system from its use. Finally, we detail the development of a real-time capable implementation of our approach using commodity GPUs and demonstrate that it outperforms a high-grade, commercial GPS-aided INS on 57km of driving in central Oxford, over a range of different conditions, times of day and year.
DOI：
10.1109/icra.2012.6224996
发表时间：
2012-05
期刊：
2012 IEEE International Conference on Robotics and Automation
影响因子：
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作者：
Ian A. Baldwin;P. Newman
通讯作者：
Ian A. Baldwin;P. Newman