Adaptive Soft-Encoding: A General Unsupervised Feature Aggregation Method for Place Recognition

• Published in: IEEE transactions on instrumentation and measurement 2023-01, Vol.72, p.1-1
• Main Authors: Gong, Yansong, Yuan, Jing, Sun, Fengchi, Sun, Qinxuan, Zhu, Wenbin, Zhang, Xuebo
• Format: Article
• Language: English
• Subjects: Adaptive soft-encoding; Coders; Coding; Data mining; Feature aggregation; Feature extraction; Image recognition; Intervals; Laser radar; Mobile robots; Place recognition; Probabilistic models; Scene analysis; Simultaneous localization and mapping; Sun; Three-dimensional displays; Training
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2023.3307752

Place recognition is an important problem in environment perception, which can help simultaneous localization and mapping (SLAM), as well as scene understanding of mobile robots. For place recognition, feature aggregation plays an important role in the representation of environments. In this paper, an adaptive soft-encoding (ASE) method is proposed for aggregating numerous features into low-dimensional feature vectors, which includes a training phase and an encoding phase. For the training phase, the information along each dimension of the features is evaluated which is further employed to assign all dimensions into different subdimensional intervals. After that, subdimensional Gaussian mixture models (SD-GMMs) are fitted by features in the subdimensional intervals and organized into an ASE tree, of which the tree nodes hierarchically store the parameters of the SD-GMMs to reflect the distribution of the features. For the encoding phase, features are fed into the root node of the ASE tree. The weight of each node is calculated from top to bottom to generate multiple aggregated feature vectors with different description capabilities. In addition, an ASE-based framework for place recognition is proposed in which local descriptors are extracted from sensor data (e.g., images or LiDAR data), and then aggregated into global descriptors by ASE. Finally, owing to the hierarchical structure of the ASE tree, a hierarchical matching strategy of the global descriptors is designed to recognize places efficiently. Experimental results demonstrate that feature vectors aggregated by ASE have strong distinguishability, and the ASE-based place recognition method achieves good accuracy and efficiency. The code of ASE can be accessed at https://github.com/wdyiwdwd/ASE-Encoder.