PMID- 38393840
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240319
IS  - 1941-0042 (Electronic)
IS  - 1057-7149 (Linking)
VI  - 33
DP  - 2024
TI  - SC_LPR: Semantically Consistent LiDAR Place Recognition Based on Chained Cascade 
      Network in Long-Term Dynamic Environments.
PG  - 2145-2157
LID - 10.1109/TIP.2024.3364511 [doi]
AB  - In large-scale long-term dynamic environments, high-frequency dynamic objects 
      inevitably lead to significant changes in the appearance of the scene at the same 
      location at different times, which is catastrophic for place recognition (PR). 
      Therefore, how to eliminate the influence of dynamic objects to achieve robust PR 
      has universal practical value for mobile robots and autonomous vehicles. To this 
      end, we suggest a novel semantically consistent LiDAR PR method based on chained 
      cascade network, called SC_LPR, which mainly consists of a LiDAR semantic image 
      inpainting network (LSI-Net) and a semantic pyramid Transformer-based PR network 
      (SPT-Net). Specifically, LSI-Net is a coarse-to-fine generative adversarial 
      network (GAN) with a gated convolutional autoencoder as the backbone. To 
      effectively address the challenges posed by variable-scale dynamic object masks, 
      we integrate the updated Transformer block with mask attention and gated trident 
      block into LSI-Net. Sequentially, in order to generate a discriminative global 
      descriptor representing the point cloud, we design an encoder with pyramid 
      Transformer block to efficiently encode long-range dependencies and global 
      contexts between different categories in the inpainted semantic image, followed 
      by an augmented NetVALD, a generalized VLAD (Vector of Locally Aggregated 
      Descriptors) layer that adaptively aggregates salient local features. Last but 
      not least, we first attempt to create a LiDAR semantic inpainting dataset, called 
      LSI-Dataset, to effectively validate the proposed method. Experimental 
      comparisons show that our method not only improves semantic inpainting 
      performance by about 6%, but also improves PR performance in dynamic environments 
      by about 8% compared to the representative optimal baseline. LSI-Dataset will be 
      publicly available at https://github.KD.LPR.com/.
FAU - Kong, Dong
AU  - Kong D
FAU - Li, Xu
AU  - Li X
FAU - Xu, Qimin
AU  - Xu Q
FAU - Hu, Yue
AU  - Hu Y
FAU - Ni, Peizhou
AU  - Ni P
LA  - eng
PT  - Journal Article
DEP - 20240318
PL  - United States
TA  - IEEE Trans Image Process
JT  - IEEE transactions on image processing : a publication of the IEEE Signal 
      Processing Society
JID - 9886191
SB  - IM
EDAT- 2024/02/23 18:42
MHDA- 2024/02/23 18:43
CRDT- 2024/02/23 12:23
PHST- 2024/02/23 18:43 [medline]
PHST- 2024/02/23 18:42 [pubmed]
PHST- 2024/02/23 12:23 [entrez]
AID - 10.1109/TIP.2024.3364511 [doi]
PST - ppublish
SO  - IEEE Trans Image Process. 2024;33:2145-2157. doi: 10.1109/TIP.2024.3364511. Epub 
      2024 Mar 18.