Typical attempts to improve the capability of visual place recognition
techniques include the use of multi-sensor fusion and integration of
information over time from image sequences. These approaches can improve
performance but have disadvantages including the need for multiple physical
sensors and calibration processes, both for multiple sensors and for tuning the
image matching sequence length. In this paper we address these shortcomings
with a novel "multi-sensor" fusion approach applied to multiple image
processing methods for a single visual image stream, combined with a dynamic
sequence matching length technique and an automatic processing method weighting
scheme. In contrast to conventional single method approaches, our approach
reduces the performance requirements of a single image processing methodology,
instead requiring that within the suite of image processing methods, at least
one performs well in any particular environment. In comparison to static
sequence length techniques, the dynamic sequence matching technique enables
reduced localization latencies through analysis of recognition quality metrics
when re-entering familiar locations. We evaluate our approach on multiple
challenging benchmark datasets, achieving superior performance to two
state-of-the-art visual place recognition systems across environmental changes
including winter to summer, afternoon to morning and night to day. Across the
four benchmark datasets our proposed approach achieves an average F1 score of
0.96, compared to 0.78 for NetVLAD and 0.49 for SeqSLAM. We provide source code
for the multi-fusion method and present analysis explaining how superior
performance is achieved despite the multiple, disparate, image processing
methods all being applied to a single source of imagery, rather than to
multiple separate sensors.

该论文提出了一种多传感器融合、动态匹配序列长度技术和自动处理方法权重方案相结合的新颖的图像处理方法，在识别质量度量分析的基础上，提高了定位的性能要求，实现了优于两种最先进的视觉地点识别系统的结果。