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SeqOT: A SpatialTemporal Transformer Network for Place Recognition Using Sequential LiDAR Data
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AI
DOI：10.1109/TIE.2022.3229385.png)
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Abstract
En 中文
Place recognition is an important component for autonomous vehicles to achieve loop closing or global localization. In this article, we tackle the problem of place recognition based on sequential 3-D LiDAR scans obtained by an onboard LiDAR sensor. We propose a transformer-based network named SeqOT to exploit the temporal and spatial information provided by sequential range images generated from the LiDAR data. It uses multiscale transformers to generate a global descriptor for each sequence of LiDAR range images in an end-to-end fashion. During online operation, our SeqOT finds similar places by matching such descriptors between the current query sequence and those stored in the map. We evaluate our approach on four datasets collected with different types of LiDAR sensors in different environments. The experimental results show that our method outperforms the state-of-the-art LiDAR-based place recognition methods and generalizes well across different environments. Furthermore, our method operates online faster than the frame rate of the sensor.
Keywords:
Laser radar
Transformers
Feature extraction
Image recognition
Fuses
Location awareness
Point cloud compression
Deep learning methods
LiDAR place recognition
sequence matching
Journal
IF:
7.2
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Citations:
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