DLC-SLAM: A Robust LiDAR-SLAM System With Learning-Based Denoising and Loop Closure

Liu, Kangcheng; Cao, Muqing

doi:10.1109/tmech.2023.3253715

Cited by 5 publications

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on the above two components, the whole system was at the same time high-performance and efficient compared with state-of-the-art approaches. Liu and Cao (2023) utilized the effective FGConv (Liu et al, 2020) as their proposed network backbone due to its high efficiency. The network adopts an encoder-decoder-based structure with skip connections.…”

Section: Introductionmentioning

confidence: 99%

Loop closure detection of visual SLAM based on variational autoencoder

Song,

Yu,

Jiang

et al. 2024

Front. Neurorobot.

View full text Add to dashboard Cite

Loop closure detection is an important module for simultaneous localization and mapping (SLAM). Correct detection of loops can reduce the cumulative drift in positioning. Because traditional detection methods rely on handicraft features, false positive detections can occur when the environment changes, resulting in incorrect estimates and an inability to obtain accurate maps. In this research paper, a loop closure detection method based on a variational autoencoder (VAE) is proposed. It is intended to be used as a feature extractor to extract image features through neural networks to replace the handicraft features used in traditional methods. This method extracts a low-dimensional vector as the representation of the image. At the same time, the attention mechanism is added to the network and constraints are added to improve the loss function for better image representation. In the back-end feature matching process, geometric checking is used to filter out the wrong matching for the false positive problem. Finally, through numerical experiments, the proposed method is demonstrated to have a better precision-recall curve than the traditional method of the bag-of-words model and other deep learning methods and is highly robust to environmental changes. In addition, experiments on datasets from three different scenarios also demonstrate that the method can be applied in real-world scenarios and that it has a good performance.

show abstract

Section: Introductionmentioning

confidence: 99%