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Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM

Ali Caglayan, Nevrez Imamoglu, Oguzhan Guclu, Ali Osman Serhatoglu, Ahmet Burak Can, Ryosuke Nakamura

TL;DR

The paper addresses the problem of robust frame association and loop closure in RGB-D SLAM by leveraging gradient-based, task-specific attention to highlight object regions. It introduces a gradient-guided feature pipeline that modulates CNN activations with layer-wise attention maps and encodes the resulting representations with Random Recursive Neural Networks into compact descriptors for indexing. Experimental results on the TUM RGB-D dataset show meaningful improvements in large-scale indoor environments, with the Direct Attention Modulation (DAM) strategy often yielding the best performance and reduced drift compared to baselines. The work demonstrates a first-step integration of attention mechanisms into SLAM representations, suggesting practical impact for improved mapping in semantically rich scenes and potential extensions to outdoor and multi-modal settings.

Abstract

Attention models have recently emerged as a powerful approach, demonstrating significant progress in various fields. Visualization techniques, such as class activation mapping, provide visual insights into the reasoning of convolutional neural networks (CNNs). Using network gradients, it is possible to identify regions where the network pays attention during image recognition tasks. Furthermore, these gradients can be combined with CNN features to localize more generalizable, task-specific attentive (salient) regions within scenes. However, explicit use of this gradient-based attention information integrated directly into CNN representations for semantic object understanding remains limited. Such integration is particularly beneficial for visual tasks like simultaneous localization and mapping (SLAM), where CNN representations enriched with spatially attentive object locations can enhance performance. In this work, we propose utilizing task-specific network attention for RGB-D indoor SLAM. Specifically, we integrate layer-wise attention information derived from network gradients with CNN feature representations to improve frame association performance. Experimental results indicate improved performance compared to baseline methods, particularly for large environments.

Exploring Object-Aware Attention Guided Frame Association for RGB-D SLAM

TL;DR

The paper addresses the problem of robust frame association and loop closure in RGB-D SLAM by leveraging gradient-based, task-specific attention to highlight object regions. It introduces a gradient-guided feature pipeline that modulates CNN activations with layer-wise attention maps and encodes the resulting representations with Random Recursive Neural Networks into compact descriptors for indexing. Experimental results on the TUM RGB-D dataset show meaningful improvements in large-scale indoor environments, with the Direct Attention Modulation (DAM) strategy often yielding the best performance and reduced drift compared to baselines. The work demonstrates a first-step integration of attention mechanisms into SLAM representations, suggesting practical impact for improved mapping in semantically rich scenes and potential extensions to outdoor and multi-modal settings.

Abstract

Attention models have recently emerged as a powerful approach, demonstrating significant progress in various fields. Visualization techniques, such as class activation mapping, provide visual insights into the reasoning of convolutional neural networks (CNNs). Using network gradients, it is possible to identify regions where the network pays attention during image recognition tasks. Furthermore, these gradients can be combined with CNN features to localize more generalizable, task-specific attentive (salient) regions within scenes. However, explicit use of this gradient-based attention information integrated directly into CNN representations for semantic object understanding remains limited. Such integration is particularly beneficial for visual tasks like simultaneous localization and mapping (SLAM), where CNN representations enriched with spatially attentive object locations can enhance performance. In this work, we propose utilizing task-specific network attention for RGB-D indoor SLAM. Specifically, we integrate layer-wise attention information derived from network gradients with CNN feature representations to improve frame association performance. Experimental results indicate improved performance compared to baseline methods, particularly for large environments.

Paper Structure

This paper contains 7 sections, 3 equations, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Proposed Method
  3. SLAM Framework
  4. Attention Guided CNN Features
  5. Random RNN for Feature Encoding
  6. Experiments
  7. Conclusion

Figures (3)

  • Figure 1: Overview of the RGB-D SLAM framework utilizing attention-guided deep features for enhanced frame association.
  • Figure 2: Detailed view of the proposed attention-guided, object-aware feature extraction process.
  • Figure 3: Comparison of estimated trajectories using the DAM attention model against ground truth for the fr1_plant, fr2_pioneer_slam, and fr2_pioneer_slam3 sequences.