Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Cho, Younggun; Kim, Ayoung

doi:10.1002/rob.21796

Cited by 16 publications

(10 citation statements)

References 48 publications

(129 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In visually degraded scenes such as turbid underwater [11] and dark scenes [12], to ensure that sufficient features are detected, image enhancement methods such as Contrast Limited Adaptive Histogram Equalization (CLAHE) or Histogram Equalization (HE) are often used in visual SLAM methods for image preprocessing. To mitigate the effects of radiation noise, adding an image denoising method is a conventional and effective solution [13].…”

Section: Image Preprocessingmentioning

confidence: 99%

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

et al. 2023

View full text Add to dashboard Cite

In the γ radioactive environment, high-energy photons induce degradation of the image sensor, which effects feature detection and tracking in the Visual Inertial Odometry (VIO) algorithm and deteriorates its localization performance. To address this issue, in this work, we propose a monocular VIO method using edge-based point features. To mitigate the effects of radiation noise, firstly, in the image preprocessing module, the median filter is used for real-time image denoising. Secondly, in the data association module, both Shi-Tomasi and edge-based point features are detected. The edge-based point feature is the endpoint or corner point in the salient edge map, which is more robust to radiation noise. Then, the bi-directional motion parallaxes and the RANdom SAmple Consensus (RANSAC) method are exploited to reject outliers. Finally, the point features measurements and Inertial Measurement Unit (IMU) pre-integration measurements are added into a tightly-coupled sliding window optimization VIO framework for localization estimation. The proposed method is verified by synthetic and real γ radioactive environment datasets. The experimental results show that the proposed method achieves more accurate and robust localization than the state-of-the-art VIO approaches in the γ radioactive environments.

show abstract

Section: Image Preprocessingmentioning

confidence: 99%

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

et al. 2023

View full text Add to dashboard Cite

show abstract

“…However, image enhancement works weaker and the MRF-based slows it down. Cho and Kim [ 15 ] exploits Doppler velocity log (DVL) sparse depth to enhance underwater images. Because planar scenes should be assumed, there are difficulties when applying to non-planar situations.…”

Section: Introductionmentioning

confidence: 99%

Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection

Yang

Lee

Kim

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

We propose an online dehazing method with sparse depth priors using an incremental Gaussian Process (iGP). Conventional approaches focus on achieving single image dehazing by using multiple channels. In many robotics platforms, range measurements are directly available, except in a sparse form. This paper exploits direct and possibly sparse depth data in order to achieve efficient and effective dehazing that works for both color and grayscale images. The proposed algorithm is not limited to the channel information and works equally well for both color and gray images. However, efficient depth map estimations (from sparse depth priors) are additionally required. This paper focuses on a highly sparse depth prior for online dehazing. For efficient dehazing, we adopted iGP for incremental depth map estimation and dehazing. Incremental selection of the depth prior was conducted in an information-theoretic way by evaluating mutual information (MI) and other information-based metrics. As per updates, only the most informative depth prior was added, and haze-free images were reconstructed from the atmospheric scattering model with incrementally estimated depth. The proposed method was validated using different scenarios, color images under synthetic fog, real color, and grayscale haze indoors, outdoors, and underwater scenes.

show abstract

“…Huang et al [24] presented a cross-modality method to associate local points with prior map's 3D structure components for better mapping efficiency, but the model's performance is relied on the accuracy of maps in the database. Similarly, Cho et al [25] introduced a deblurred and dehazing technique method to improve the quality of frames in SLAM. Jin [26] proposed a lightweight convolution neural network-based corner point extractor instead of FAST, which improved the localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

DT-SLAM: Dynamic Thresholding Based Corner Point Extraction in SLAM System

Pike

Lee

2021

IEEE Access

View full text Add to dashboard Cite

Visual localization estimation is highly depended on the quality of video frames or captured images. Estimation quality may be affected by the poor visibility, low background texture and overexposure. Low quality frames with blurred edges and poor contrast pose tremendous difficulties for corner points detection in SLAM impacting the overall accuracy of estimation. This paper introduces DT-SLAM, a dynamic self-adaptive threshold (DSAT) approach for ORB corner points extraction in FAST to improve SLAM's localization performance. The proposed method replaces the existing static threshold-based ORB extraction approach, enabling improved performance in complex real-world scenes. In addition, this study introduces a threshold switching mechanism (TSM) to replace the existing SLAM's frame-level and celllevel thresholds for corner points extraction. The proposed DT-SLAM approach is validated using the TUM RBG-D and EuRoC benchmark datasets for location tracking performances. The results indicate that the proposed DT-SLAM (mean RM SE = 0.0807 (EuRoC); mean RM SE = 0.0153 (TUM)) outperforming the current state-of-the-art ORB-SLAM3 (mean RM SE = 0.1899 (EuRoC); mean RM SE = 0.0252 (TUM)).

show abstract

Channel invariant online visibility enhancement for visual SLAM in a turbid environment

Cited by 16 publications

References 48 publications

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

Robust monocular visual inertial odometry in γ radioactive environments using edge-based point features

Sparse Depth-Guided Image Enhancement Using Incremental GP with Informative Point Selection

DT-SLAM: Dynamic Thresholding Based Corner Point Extraction in SLAM System

Contact Info

Product

Resources

About