论文标题
DeepSeqslam：可训练的CNN+RNN，用于联合全球描述和基于序列的位置识别
DeepSeqSLAM: A Trainable CNN+RNN for Joint Global Description and Sequence-based Place Recognition
论文作者
论文摘要
全天候导航的基于序列的位置识别方法以在充满挑战的昼夜或夏季冬季过渡下产生最先进的结果而闻名。但是，这些系统依赖于复杂的手工启发式方法进行顺序匹配 - 与单帧检索方法相比，单个路线的参考和查询图像序列之间的预计成对相似性矩阵的顶部都应用了假阳性率。结果，执行多帧地点识别的识别对于在自动驾驶汽车上部署或在大型数据集上进行评估可能非常慢，并且在使用相对较短的参数值（例如序列长度为2帧）时失败。在本文中，我们提出了DeepSeqslam：一种可训练的CNN+RNN架构，用于从途径的单眼图像序列中共同学习视觉和位置表示。我们在两个大型基准数据集（Nordland and Oxford Robotcar）上展示了我们的方法，分别记录了728公里和10公里的路线，各个季节，天气和照明条件。在诺德兰，我们将我们的方法与夏季冬季的两种基于最先进的序列方法在整个路线上使用2的序列长度进行了比较，并表明我们的方法可以达到72％以上的AUC，而Delta描述符的27％AUC和Seqslam的2％AUC和2％AUC；同时将部署时间从大约1小时减少到1分钟。框架代码和视频可从https://mchancan.github.io/deepseqslam获得
Sequence-based place recognition methods for all-weather navigation are well-known for producing state-of-the-art results under challenging day-night or summer-winter transitions. These systems, however, rely on complex handcrafted heuristics for sequential matching - which are applied on top of a pre-computed pairwise similarity matrix between reference and query image sequences of a single route - to further reduce false-positive rates compared to single-frame retrieval methods. As a result, performing multi-frame place recognition can be extremely slow for deployment on autonomous vehicles or evaluation on large datasets, and fail when using relatively short parameter values such as a sequence length of 2 frames. In this paper, we propose DeepSeqSLAM: a trainable CNN+RNN architecture for jointly learning visual and positional representations from a single monocular image sequence of a route. We demonstrate our approach on two large benchmark datasets, Nordland and Oxford RobotCar - recorded over 728 km and 10 km routes, respectively, each during 1 year with multiple seasons, weather, and lighting conditions. On Nordland, we compare our method to two state-of-the-art sequence-based methods across the entire route under summer-winter changes using a sequence length of 2 and show that our approach can get over 72% AUC compared to 27% AUC for Delta Descriptors and 2% AUC for SeqSLAM; while drastically reducing the deployment time from around 1 hour to 1 minute against both. The framework code and video are available at https://mchancan.github.io/deepseqslam
