TY - JOUR
T1 - ConvSequential-SLAM
T2 - A Sequence-Based, Training-Less Visual Place Recognition Technique for Changing Environments
AU - Tomia, Mihnea Alexandru
AU - Zaffar, Mubariz
AU - Milford, Michael J.
AU - McDonald-Maier, Klaus D.
AU - Ehsan, Shoaib
PY - 2021
Y1 - 2021
N2 - 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 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 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.
AB - 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 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 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.
KW - sequence-based filtering
KW - SLAM
KW - visual localization
KW - visual place recognition
UR - http://www.scopus.com/inward/record.url?scp=85113834435&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3107778
DO - 10.1109/ACCESS.2021.3107778
M3 - Article
AN - SCOPUS:85113834435
SN - 2169-3536
VL - 9
SP - 118673
EP - 118683
JO - IEEE Access
JF - IEEE Access
ER -