Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection (1504.05241v1)

Abstract: Deep convolutional neural networks (CNN) have recently been shown in many computer vision and pattern recog- nition applications to outperform by a significant margin state- of-the-art solutions that use traditional hand-crafted features. However, this impressive performance is yet to be fully exploited in robotics. In this paper, we focus one specific problem that can benefit from the recent development of the CNN technology, i.e., we focus on using a pre-trained CNN model as a method of generating an image representation appropriate for visual loop closure detection in SLAM (simultaneous localization and mapping). We perform a comprehensive evaluation of the outputs at the intermediate layers of a CNN as image descriptors, in comparison with state-of-the-art image descriptors, in terms of their ability to match images for detecting loop closures. The main conclusions of our study include: (a) CNN-based image representations perform comparably to state-of-the-art hand- crafted competitors in environments without significant lighting change, (b) they outperform state-of-the-art competitors when lighting changes significantly, and (c) they are also significantly faster to extract than the state-of-the-art hand-crafted features even on a conventional CPU and are two orders of magnitude faster on an entry-level GPU.

• The paper proposes using CNN-based image representations for visual loop closure detection, finding they are highly robust to variable lighting and computationally efficient compared to hand-crafted features.
• CNN descriptors, particularly from layers like POOL5, significantly outperform traditional methods like BoVW and GIST under changing illumination, while performing comparably to advanced features in stable light.
• CNN-based features offer substantial computational efficiency, being orders of magnitude faster on CPU and GPU than hand-crafted methods, making them practical for real-time SLAM applications.

Convolutional Neural Network-Based Image Representation for Visual Loop Closure Detection

In the domain of simultaneous localization and mapping (SLAM), the task of visual loop closure detection presents challenges, especially in dynamic environments with fluctuating illumination conditions. This paper addresses these issues by employing convolutional neural network (CNN)-based image representations, which have demonstrated superior performance in various computer vision tasks, to enhance loop closure detection in SLAM.

The authors have conducted a comprehensive evaluation comparing CNN-generated image descriptors with traditional hand-crafted ones, assessing their ability to detect loop closures amidst varying lighting conditions. The work leverages a pre-trained CNN model known as the Places-CNN, designed for scene classification, to generate whole-image descriptors from the intermediate layers of the network. Their findings highlight several key points:

1. Performance in Variable Lighting: CNN-based image descriptors show remarkable robustness to lighting variations, outperforming hand-crafted features under changing illumination conditions. For instance, the paper reports that CNN descriptors extracted from layers like POOL5 maintain high accuracy and invariance, contrasting with the sensitivity of traditional descriptors like BoVW and GIST to such changes.
2. Comparison with Hand-Crafted Descriptors: In environments with stable lighting, CNN descriptors perform comparably to advanced hand-crafted descriptors, such as FV and VLAD. However, once illumination shifts, the CNN-based features exhibit a notable advantage.
3. Efficiency and Computational Cost: The paper emphasizes the computational efficiency of CNN-based features. On a CPU, they are found to be an order of magnitude faster than hand-crafted counterparts, a benefit that escalates to two orders of magnitude when employing an entry-level GPU.

The implications of this research for the SLAM community are twofold. Practically, the findings advocate for integrating CNN-based image descriptors in loop closure detection systems, particularly for long-term autonomous navigation where lighting variability is prevalent. Theoretically, it prompts further exploration into leveraging deep learning architectures, such as auto-encoding and dimensionality reduction, for more efficient and robust visual SLAM solutions.

Furthermore, the paper sets a precedent for utilizing pre-trained models from other domains, like scene recognition, adapting them through fine-tuning to specific tasks such as loop closure detection. This cross-domain application of pre-trained models could lead to a more generalized and effective approach in robotics and autonomous systems.

Future directions proposed by the authors include dimensionality reduction for compact feature representation, the deployment of deep-learning techniques to enhance discriminative capabilities, and the training of domain-specific CNN models fine-tuned for visual SLAM.

In sum, this paper makes a compelling case for the adoption of CNN-based image descriptors in visual loop closure detection, capitalizing on their abstraction capabilities, computational efficiency, and adaptability, paving the way for advancements in autonomous navigation.