Jacobson Adam, Chen Zetao, Milford Michael
School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia.
Biol Cybern. 2018 Jun;112(3):209-225. doi: 10.1007/s00422-017-0745-7. Epub 2018 Jan 20.
Most robot navigation systems perform place recognition using a single-sensor modality and one, or at most two heterogeneous map scales. In contrast, mammals perform navigation by combining sensing from a wide variety of modalities including vision, auditory, olfactory and tactile senses with a multi-scale, homogeneous neural map of the environment. In this paper, we develop a multi-scale, multi-sensor system for mapping and place recognition that combines spatial localization hypotheses at different spatial scales from multiple different sensors to calculate an overall place recognition estimate. We evaluate the system's performance over three repeated 1.5-km day and night journeys across a university campus spanning outdoor and multi-level indoor environments, incorporating camera, WiFi and barometric sensory information. The system outperforms a conventional camera-only localization system, with the results demonstrating not only how combining multiple sensing modalities together improves performance, but also how combining these sensing modalities over multiple scales further improves performance over a single-scale approach. The multi-scale mapping framework enables us to analyze the naturally varying spatial acuity of different sensing modalities, revealing how the multi-scale approach captures each sensing modality at its optimal operation point where a single-scale approach does not, and enables us to then weight sensor contributions at different scales based on their utility for place recognition at that scale.
大多数机器人导航系统使用单一传感器模态以及一种或至多两种异构地图尺度来执行地点识别。相比之下，哺乳动物通过将来自多种模态（包括视觉、听觉、嗅觉和触觉）的感知与环境的多尺度、同构神经地图相结合来进行导航。在本文中，我们开发了一种用于地图绘制和地点识别的多尺度、多传感器系统，该系统结合了来自多个不同传感器在不同空间尺度上的空间定位假设，以计算整体地点识别估计值。我们在跨越室外和多层室内环境的大学校园进行的三次重复的1.5公里昼夜行程中评估了该系统的性能，纳入了摄像头、WiFi和气压传感信息。该系统优于传统的仅使用摄像头定位系统，结果不仅表明将多种传感模态结合在一起如何提高性能，还表明将这些传感模态在多个尺度上结合如何比单尺度方法进一步提高性能。多尺度地图绘制框架使我们能够分析不同传感模态自然变化的空间敏锐度，揭示多尺度方法如何在单尺度方法无法做到的情况下在其最佳操作点捕捉每种传感模态，并使我们能够根据传感器在该尺度上对地点识别的效用在不同尺度上加权传感器贡献。
