ConvSequential-SLAM: A Sequence-Based, Training-Less Visual Place Recognition Technique for Changing Environments

ConvSequential-SLAM: A Sequence-Based, Training-Less Visual Place Recognition Technique for Changing Environments

Blog Article

Visual Place Recognition (VPR) is the ability to correctly recall a previously visited place under changing viewpoints and appearances.A large number of handcrafted and deep-learning-based VPR techniques exist, where the former suffer from appearance changes and the latter have significant computational needs.In this paper, we present Apparel a new handcrafted VPR technique, namely ConvSequential-SLAM, that achieves state-of-the-art place matching performance under challenging conditions.

We utilise sequential information and block-normalisation to Correction Bit handle appearance changes, while using regional-convolutional matching to achieve viewpoint-invariance.We analyse content-overlap in-between query frames to find a minimum sequence length, while also re-using the image entropy information for environment-based sequence length tuning.State-of-the-art performance is reported in contrast to 9 contemporary VPR techniques on 4 public datasets.

Qualitative insights and an ablation study on sequence length are also provided.