论文标题
学会通过压缩二进制图定位
Learning to Localize Through Compressed Binary Maps
论文作者
论文摘要
将当前本地化系统扩展到大环境的主要困难之一是地图所需的板载存储。在本文中，我们建议学习压缩地图表示，以使其最适合本地化任务。结果，与优化重建的标准编码方案相比，可以实现较高的压缩率，而不会损失本地化精度，从而忽略了最终任务。我们的实验表明，可以学习特定于任务的压缩，该压缩将存储要求减少了两个数量级，而不是通用编解码器，例如WebP，而无需牺牲性能。
One of the main difficulties of scaling current localization systems to large environments is the on-board storage required for the maps. In this paper we propose to learn to compress the map representation such that it is optimal for the localization task. As a consequence, higher compression rates can be achieved without loss of localization accuracy when compared to standard coding schemes that optimize for reconstruction, thus ignoring the end task. Our experiments show that it is possible to learn a task-specific compression which reduces storage requirements by two orders of magnitude over general-purpose codecs such as WebP without sacrificing performance.
