Abstract
Localization is one of the essential process in robotics, as it plays an important role in autonomous navigation, simultaneous localization, and mapping for mobile robots. As robots perform large-scale and long-term operations, identifying the same locations in a changing environment has become an important problem. In this paper, we describe a robust visual localization system under severe appearance changes. First, a robust feature extraction method based on a deep variational autoencoder is described to calculate the similarity between images. Then, a global sequence alignment is proposed to find the actual trajectory of the robot. To align sequences, local fragments are detected from the similarity matrix and connected using a rectangle chaining algorithm considering the robot’s motion constraint. Since the chained fragments provide reliable clues to find the global path, false matches on featureless structures or partial failures during the alignment could be recovered and perform accurate robot localization in changing environments. The presented experimental results demonstrated the benefits of the proposed method, which outperformed existing algorithms in long-term conditions.
Highlights
This paper presents a robust feature extraction method using a deep architecture in first part, and a novel sequence alignment algorithm to perform precise localization in second part
We propose glocal sequence alignment, a combination of the global and local alignment methods, which arranges the sequences of features to perform precise localization
Local seeds were detected above the similarity 0.99 and performed local sequence alignment using the dynamic programming (DP) [33]
Summary
This paper presents a robust feature extraction method using a deep architecture in first part, and a novel sequence alignment algorithm to perform precise localization in second part. After generating the similarity matrix, the most likely path sequence of the robot should be estimated from the matrix to perform precise localization. These sequence-based approaches achieved significant improvements in place recognition by attempting to match sequences rather than single images [15,16]. The feature extraction method from the VAE and the proposed glocal sequence algorithm is discussed .
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