An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation
<p>(<b>a</b>) High resolution SSS image recorded with the DE340D SSS at Stockholm sea [<a href="#B40-sensors-16-01148" class="html-bibr">40</a>]; (<b>b</b>) Low resolution SSS image generated by the 3500 Klein SSS (ECA Group company [<a href="#B41-sensors-16-01148" class="html-bibr">41</a>]).</p> "> Figure 2
<p>The procedure of the improved Otsu TSM.</p> "> Figure 3
<p>The plots of the power-law equation with different <span class="html-italic">r</span> values.</p> "> Figure 4
<p>(<b>a</b>) Traditional Otsu TSM, <span class="html-italic">Th</span> = 0.3216; (<b>b</b>) Local TSM, <span class="html-italic">Th</span> = 0.1628; (<b>c</b>) Iterative TSM, <span class="html-italic">Th</span> = 0.4238; (<b>d</b>) Maximum entropy TSM, <span class="html-italic">Th</span> = 0.6627.</p> "> Figure 5
<p>(<b>a</b>) Canny edge detection after applying the traditional Otsu method, bw = edge (b, ‘canny’, 0.33), <span class="html-italic">N</span><sub>30</sub> = 752 > 300; (<b>b</b>) Improved Otsu TSM,<span class="html-italic">T</span> = 0.3216, <span class="html-italic">T</span>* = 0.6784; (<b>c</b>) Result of the improved Otsu TSM after morphological operations marking the centroids of the obtained regions; (<b>d</b>) Result of the maximum entropy TSM after the same morphological operations marking the centroids of the acquired areas.</p> "> Figure 6
<p>(<b>a</b>) Traditional Otsu TSM, <span class="html-italic">Th</span> = 0.1137; (<b>b</b>) Local TSM, <span class="html-italic">Th</span> = 0.0941; (<b>c</b>) Iterative TSM, <span class="html-italic">Th</span> = 0.2609; (<b>d</b>) Maximum entropy TSM, <span class="html-italic">Th</span> = 0.3176.</p> "> Figure 7
<p>(<b>a</b>) Canny contour detection after applying the traditional Otsu method, bw=edge (b, ‘canny’, 0.1255), <span class="html-italic">N</span><sub>15</sub> = 419 > 100; (<b>b</b>) Improved Otsu TSM, <span class="html-italic">T</span> = 0.1137, T* = 0.3529; (<b>c</b>) Result of the improved Otsu TSM after morphological operations marking the centroids of the obtained regions; (<b>d</b>) Result of the maximum entropy TSM after the same morphological operations marking the centroids of the acquired areas.</p> "> Figure 8
<p>The original FLS image comes from [<a href="#B48-sensors-16-01148" class="html-bibr">48</a>], and there is a plastic mannequin in the down center.</p> "> Figure 9
<p>(<b>a</b>) Traditional Otsu TSM, <span class="html-italic">Th</span> = 0.1176; (<b>b</b>) Local TSM, <span class="html-italic">Th</span> = 0.0941; (<b>c</b>) Iterative TSM, <span class="html-italic">Th</span> = 0.2990; (<b>d</b>) Maximum entropy TSM, <span class="html-italic">Th</span> = 0.4118.</p> "> Figure 10
<p>(<b>a</b>) Canny edge detection after employing the traditional Otsu method, bw = edge (b, ‘canny’, 0.13), <span class="html-italic">N</span><sub>40</sub> = 1341 > 600; (<b>b</b>) Improved Otsu TSM, <span class="html-italic">T</span> = 0.1176, <span class="html-italic">T</span>* = 0.5412; (<b>c</b>) Result of the improved Otsu TSM after morphological operations marking the centroids of the acquired areas; (<b>d</b>) Result of the maximum entropy TSM after the same morphological operations marking the centroids of the obtained regions.</p> "> Figure 11
<p>The flow chart of SLAM procedure based on an AEKF. Modified after [<a href="#B27-sensors-16-01148" class="html-bibr">27</a>].</p> "> Figure 12
<p>The architecture of the AEKF-SLAM system, as described in [<a href="#B50-sensors-16-01148" class="html-bibr">50</a>].</p> "> Figure 13
<p>(<b>a</b>) The robot is observing the centroids of certain parts of the body before loop closure; (<b>b</b>) The final AEKF-SLAM loop map where the landmarks are detected by the improved Otsu TSM.</p> "> Figure 14
<p>(<b>a</b>) The robot is observing the centroids of certain parts of the body before loop closure; (<b>b</b>) The final AEKF-SLAM loop map where the landmarks are detected by the maximum entropy TSM.</p> ">
Abstract
:1. Introduction
2. Related Works
2.1. Map Representations
2.2. Simultaneous Localization and Mapping
3. An Improved Otsu TSM for Fast Feature Detection
3.1. Side-Scan Sonar Images
3.2. The Proposed Improved Otsu TSM Algorithm
3.3. The Power-Law Transformation
3.4. TSM Results for Side-Scan Sonar Images
3.4.1. TSM Results for High Resolution SSS Image
Algorithm 1: Canny edge detection |
1. Smooth the image with a Gaussian filter, h = fspecial (‘gaussian’, [3 3], 0.5); |
2. Calculate the gradient’s amplitude and orientation with the finite-difference for the first partial derivative; |
3. Non-Maxima Suppression; |
4. Detect and link the edge with double threshold method, y = edge (b, ‘canny’, 0.33), the high threshold for Figure 1a is 0.33, and the 0.4 times high threshold is used for the low threshold. |
Algorithm 2: Morphological operations for detecting feature centroids |
1. Remove all connected components that have fewer than 30 pixels in Figure 5b; |
2. Bridge previously unconnnected pixels; |
3. Perform dilation using the structuring element ones (3) with the size of a 3 × 3 square; |
4. Fill the holes in the image; |
5. Compute the area size, the centroid and the bounding box of different contiguous regions; |
6. Concatenate structure array which contains all centroids into a single matrix. |
3.4.2. TSM Results for Low Resolution SSS Image
3.5. TSM Results for Forward-Looking Sonar Image
4. The Estimation-Theoretic AEKF-SLAM Approach
4.1. Extended Kalman Filter
- Time Update
- Predictor step:The nonlinear functions f and h are linearized by using a Taylor series expansion, where terms of second and higher order are omitted.
- Measurement Update
- •
- Calculate the Kalman gain Kk, .
- •
- Corrector step:
- First, update the expected value , .
- Then, update the error covariance matrix , .
4.2. The Estimation Process of the AEKF-SLAM
Algorithm 3: Underwater landmark map building based on AEKF-SLAM |
1. |
2. |
3. |
4. |
5. |
6. |
7. |
4.2.1. Prediction Stage
4.2.2. Update Stage
4.2.3. State Augmentation
4.3. AEKF-SLAM Loop Map Simulation
5. Conclusions and Future Work
5.1. Conclusions
5.2. Future Work
- •
- Employing other forms of target objects for the detection and tracking purpose, devising parametric feature models for describing general objects, and more complex scenarios with multiple distinct features will also be included. Besides, more complicated vehicle model such as six DOF kinematic model will be investigated. Therefore, as the robot navigates, we can perform the proposed feature detection algorithm on the acquired images exactly when the 3D object is detected by the sonar.
- •
- Developing a computationally tractable version of the SLAM map building algorithm which maintains the properties of being consistent and non-divergent. Hierarchical SLAM or sub-mapping methods build local maps of limited size, which bound the covariances and thus the linearization errors. Then, by linking the local maps through a global map or a hierarchy of global maps, AEKF-based SLAM application in large environments is possible.
- •
- Considering the application of unscented KF (UKF) in the field of underwater robotic navigation. As an alternative estimation technique, UKF does not need calculating the derivatives, and it also handles a very effective tradeoff between computational load and estimation accuracy in the case of strongly nonlinear and discontinuous systems [54]. Besides, considering FastSLAM, which uses the Rao-Blackwellised method for particle filtering (RBPF), as future work, since it is very suitable for non-linear motions. It also has better performance than EKF-SLAM at solving the data association problem for detecting loop closures. Afterwards, we will evaluate the estimation performances of these two methods to the SLAM problem with that of the AEKF considered in this work.
- •
- Incorporating data streams observed from the acoustic and visual sensors to generate a 3D representation of the underwater environment, i.e., the seabed, working environment or artifacts [55]. In our case, we will use the depth logger based on pressure for navigation and the DE340D SSS as perception sensor to get horizontal positions of features of interest, therefore by integrating with the vertical positioning data obtained through pressure sensor, a subsea 3D map will be created.
- •
- Considering map simplification and transform based algorithms for fusion of two different resolution maps. One is a large scale medium resolution map generated using a SSS (in SWARMs T4.1 Large scale 3D mapping), the other is a local 3D high resolution map created by fusion of FLS images and visual information. The sonar system used to obtain the large scale map achieves a very high area coverage rate but has a modest resolution, as it could detect objects but is insufficient to identify their precise nature. To achieve combining both systems for maximizing the operational effectiveness, the large scale medium resolution map will be used to trigger detailed investigations of regions of interest using the local 3D high resolution maps.
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
SONAR | SOund Navigation And Ranging |
SLAM | Simultaneous Localization and Mapping |
TSM | Threshold Segmentation Method |
SSS | Side-Scan Sonar |
FLS | Forward-Looking Sonar |
AEKF | Augmented Extended Kalman Filter |
KF | Kalman Filter |
EKF | Extended Kalman Filter |
PF | Particle Filter |
EM | Expectation Maximization |
SIFT | Scale-Invariant Feature Transform |
SURF | Speeded Up Robust Features |
DOF | Degree of Freedom |
CML | Concurrent Mapping and Localization |
RSSI | Received Signal Strength Indication |
AUV | Autonomous Underwater Vehicle |
FRR | False Positive Rate |
PPV | Positive Predictive Value |
RMS | Root Mean Square |
RBPF | Rao-Blackwellised Particle Filtering |
UKF | Unscented Kalman Filter |
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Detected | |||
---|---|---|---|
Ship Centroids | Non-Ship Centroids | ||
Real | Ship Centroids | 2 | 0 |
Non-Ship Centroids | 4 | 21 |
Detected | |||
---|---|---|---|
Ship Centroids | Non-Ship Centroids | ||
Real | Ship Centroids | 2 | 0 |
Non-Ship Centroids | 8 | 20 |
Segmentation Method | Computational Time [s] |
---|---|
Traditional Otsu TSM | 0.178226 |
Local TSM | 0.913942 |
Iterative TSM | 0.289513 |
Maximum entropy TSM | 1.562499 |
Improved Otsu TSM | 0.868372 |
Detected | |||
---|---|---|---|
Branch Centroids | Non-Branch Centroids | ||
Real | Branch Centroids | 1 | 0 |
Non-Branch Centroids | 1 | 13 |
Detected | |||
---|---|---|---|
Branch Centroids | Non-Branch Centroids | ||
Real | Branch Centroids | 1 | 0 |
Non-Branch Centroids | 7 | 11 |
Segmentation Method | Computational Time [s] |
---|---|
Traditional Otsu TSM | 0.120458 |
Local TSM | 0.261021 |
Iterative TSM | 0.227290 |
Maximum entropy TSM | 0.378283 |
Improved Otsu TSM | 0.241164 |
Detected | |||
---|---|---|---|
Body Centroids | Non-Body Centroids | ||
Real | Body Centroids | 5 | 0 |
Non-Body Centroids | 11 | 26 |
Detected | |||
---|---|---|---|
Body Centroids | Non-Body Centroids | ||
Real | Body Centroids | 2 | 3 |
Non-Body Centroids | 44 | 40 |
Segmentation Method | Computational Time [s] |
---|---|
Traditional Otsu TSM | 0.244472 |
Local TSM | 0.941853 |
Iterative TSM | 0.428126 |
Maximum entropy TSM | 3.903889 |
Improved Otsu TSM | 1.452562 |
Ship [m] | Branch [m] | Body [m] | ||||||
---|---|---|---|---|---|---|---|---|
True | (53.5, 60.3) | (54.23, 65.39) | (18.73, −11.56) | (−94.98, −66.29) | (−96.69, −66.06) | (−102.12, −61.57) | (−102.41, −70.3) | (−106.55, −81.13) |
Estimated | (53.66, 60.23) | (54.31, 65.32) | (18.8, −11.49) | (−94.99, −66.35) | (−96.67, −66.12) | (−102.2, −61.59) | (−102.4, −70.34) | (−106.4, −81.44) |
Ship [m] | Branch [m] | Body [m] | |||
---|---|---|---|---|---|
True | (53.61, 60.18) | (54.22, 65.4) | (18.75, −11.55) | (−99.23, −67.7) | (−97.59, −72.08) |
Estimated | (54.24, 59.12) | (54.96, 64.35) | (18.62, −11.69) | (−100.1, −65.82) | (−98.58, −70.23) |
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Yuan, X.; Martínez, J.-F.; Eckert, M.; López-Santidrián, L. An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation. Sensors 2016, 16, 1148. https://doi.org/10.3390/s16071148
Yuan X, Martínez J-F, Eckert M, López-Santidrián L. An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation. Sensors. 2016; 16(7):1148. https://doi.org/10.3390/s16071148
Chicago/Turabian StyleYuan, Xin, José-Fernán Martínez, Martina Eckert, and Lourdes López-Santidrián. 2016. "An Improved Otsu Threshold Segmentation Method for Underwater Simultaneous Localization and Mapping-Based Navigation" Sensors 16, no. 7: 1148. https://doi.org/10.3390/s16071148