Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation

• URL: http://arxiv.org/abs/2510.20549v1
• Date: Thu, 23 Oct 2025 13:35:12 GMT
• Title: Deep Learning-Powered Visual SLAM Aimed at Assisting Visually Impaired Navigation
• Abstract summary: We present SELM-SLAM3, a deep learning-enhanced visual SLAM framework that integrates SuperPoint and LightGlue for robust feature extraction and matching.<n>Our framework demonstrates enhanced performance under challenging conditions, such as low-texture scenes and fast motion, providing a reliable platform for developing navigation aids for the visually impaired.
• Score: 3.5174884177930448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite advancements in SLAM technologies, robust operation under challenging conditions such as low-texture, motion-blur, or challenging lighting remains an open challenge. Such conditions are common in applications such as assistive navigation for the visually impaired. These challenges undermine localization accuracy and tracking stability, reducing navigation reliability and safety. To overcome these limitations, we present SELM-SLAM3, a deep learning-enhanced visual SLAM framework that integrates SuperPoint and LightGlue for robust feature extraction and matching. We evaluated our framework using TUM RGB-D, ICL-NUIM, and TartanAir datasets, which feature diverse and challenging scenarios. SELM-SLAM3 outperforms conventional ORB-SLAM3 by an average of 87.84% and exceeds state-of-the-art RGB-D SLAM systems by 36.77%. Our framework demonstrates enhanced performance under challenging conditions, such as low-texture scenes and fast motion, providing a reliable platform for developing navigation aids for the visually impaired.

Related papers

• LiveLight: Real-time Streaming Video Relighting with Interactive Control [50.08584152649119]
  We present LiveLight, the first diffusion-based framework for real-time streaming video relighting with interactive 3D lighting control.<n>We overcome three critical challenges: effectively injecting dynamic 3D lighting into a diffusion model, maintaining high-fidelity generation under a low NFE budget, and facilitating continuous streaming for interactive control.<n>Experiments on real-world and synthetic benchmarks demonstrate that LiveLight achieves state-of-the-art relighting quality while running at real-time speed.
  arXiv  Detail & Related papers  (2026-08-03T06:45:00Z)
• Edged USLAM: Edge-Aware Event-Based SLAM with Learning-Based Depth Priors [1.504944269180605]
  Event cameras mitigate these issues with high temporal resolution and high dynamic range.<n>We present Edged USLAM, a hybrid visual-inertial system that extends Ultimate SLAM with an edge-aware front-end and a lightweight depth module.
  arXiv  Detail & Related papers  (2026-03-09T09:28:52Z)
• VIGS-SLAM: Visual Inertial Gaussian Splatting SLAM [75.55522219717137]
  We present VIGS-SLAM, a visual-inertial 3D Gaussian Splatting SLAM system.<n>It achieves robust real-time tracking and high-fidelity reconstruction.<n>Our method tightly couples visual and inertial cues within a unified optimization framework.
  arXiv  Detail & Related papers  (2025-12-02T00:19:13Z)
• LIR-LIVO: A Lightweight,Robust LiDAR/Vision/Inertial Odometry with Illumination-Resilient Deep Features [8.095827028713684]
  The proposed method leverages deep learning-based illumination-resilient features and LiDAR-Inertial-Visual Odometry (LIVO)<n>LIR-LIVO achieves state-of-the-art (SOTA) accuracy and robustness with low computational cost.
  arXiv  Detail & Related papers  (2025-02-12T05:28:10Z)
• ROVER: A Multi-Season Dataset for Visual SLAM [7.296917102476635]
  ROVER is a benchmark dataset for evaluating visual SLAM algorithms in diverse environmental conditions.<n>It covers 39 recordings across five outdoor locations, collected through all seasons and various lighting scenarios.<n>Results show that while stereo-inertial and RGBD configurations perform better under favorable lighting, most SLAM systems perform poorly in low-light and high-vegetation scenarios.
  arXiv  Detail & Related papers  (2024-12-03T15:34:00Z)
• FlashSLAM: Accelerated RGB-D SLAM for Real-Time 3D Scene Reconstruction with Gaussian Splatting [14.130327598928778]
  FlashSLAM is a novel SLAM approach that leverages 3D Gaussian Splatting for efficient and robust 3D scene reconstruction.<n>Existing 3DGS-based SLAM methods often fall short in sparse view settings and during large camera movements.<n>Our method achieves up to a 92% improvement in average tracking accuracy over previous methods.
  arXiv  Detail & Related papers  (2024-12-01T05:44:38Z)
• QueensCAMP: an RGB-D dataset for robust Visual SLAM [0.0]
  We introduce a novel RGB-D dataset designed for evaluating the robustness of VSLAM systems. The dataset comprises real-world indoor scenes with dynamic objects, motion blur, and varying illumination. We offer open-source scripts for injecting camera failures into any images, enabling further customization.
  arXiv  Detail & Related papers  (2024-10-16T12:58:08Z)
• LiDAR-GS:Real-time LiDAR Re-Simulation using Gaussian Splatting [53.58528891081709]
  We present LiDAR-GS, a real-time, high-fidelity re-simulation of LiDAR scans in public urban road scenes.<n>The method achieves state-of-the-art results in both rendering frame rate and quality on publically available large scene datasets.
  arXiv  Detail & Related papers  (2024-10-07T15:07:56Z)
• Real-Time Multi-Scene Visibility Enhancement for Promoting Navigational Safety of Vessels Under Complex Weather Conditions [48.529493393948435]
  The visible-light camera has emerged as an essential imaging sensor for marine surface vessels in intelligent waterborne transportation systems. The visual imaging quality inevitably suffers from several kinds of degradations under complex weather conditions. We develop a general-purpose multi-scene visibility enhancement method to restore degraded images captured under different weather conditions.
  arXiv  Detail & Related papers  (2024-09-02T23:46:27Z)
• NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM [111.83168930989503]
  NICER-SLAM is a dense RGB SLAM system that simultaneously optimize for camera poses and a hierarchical neural implicit map representation. We show strong performance in dense mapping, tracking, and novel view synthesis, even competitive with recent RGB-D SLAM systems.
  arXiv  Detail & Related papers  (2023-02-07T17:06:34Z)
• Low-Light Hyperspectral Image Enhancement [90.84144276935464]
  This work focuses on the low-light HSI enhancement task, which aims to reveal the spatial-spectral information hidden in darkened areas. Based on Laplacian pyramid decomposition and reconstruction, we developed an end-to-end data-driven low-light HSI enhancement (HSIE) approach. The effectiveness and efficiency of HSIE both in quantitative assessment measures and visual effects are demonstrated.
  arXiv  Detail & Related papers  (2022-08-05T08:45:52Z)
• Toward Fast, Flexible, and Robust Low-Light Image Enhancement [87.27326390675155]
  We develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage. We make comprehensive explorations to SCI's inherent properties including operation-insensitive adaptability and model-irrelevant generality.
  arXiv  Detail & Related papers  (2022-04-21T14:40:32Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.