Adaptive Multi-Sensor Fusion Localization Method Based on Filtering

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In this manuscript, the authors are addressing the issue of low positioning accuracy and poor robustness in single sensor positioning within complex environments. The issue is tackled by proposing a multi-level fuzzy evaluation model for posture transformation states. Further, an adaptive multi-sensor fusion positioning method, based on the error state Kalman filter, is introduced to evaluate data from GNSS and laser inertial odometers. The approach facilitates the adaptive updating of posture. The article is interesting. However, I have following minor concerns:

 

1. The proposed results are tacking the real scenarios. However, the resolutions of figures presented in results section is not adequate. Please re-prepare results to better visibility.

2. Eq. (10) should separately represent H1 and H2 for better representation.

3. Mu and Eta should also be separated in Eq. (5).

4. A short organization may also be added in the introduction section, for better overview of overall manuscript.

5. The error state Kalman filter may be discussed in more detail in the introduction.

 

Author Response

Please see the attachment

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The proposed method and results are promising, but the manuscript requires significant improvements in clarity, detail, and depth of analysis to meet the standards for publication. I recommend a Major Revision for this paper. Here are some comments:

- Please check the quality of Figure 1.

- The comparison with other algorithms, such as LIO-SAM and GNSS/IMU, lacks sufficient quantitative metrics. Could you provide more detailed statistical comparisons, such as RMSE or other relevant error metrics?

- The real-world testing scenarios described (e.g., tunnel, viaduct) are interesting, but the exact conditions and variables controlled during these tests are not detailed. 

- Provide a clearer step-by-step explanation or a flowchart to improve understanding of Kalman filter and its implimentation?

- The selection of first-level evaluation factors A=[a1,a2] is briefly mentioned. What criteria were used to determine these factors, and why were these specific factors chosen? Could you explain the rationale behind the specific membership function values chosen? 

- The weights t1,t2,t3 for the GNSS data are given as [0.4, 0.4, 0.2]. How were these weights determined, and what is their impact on the overall evaluation?

- Please discuss how the proposed method advances or differs significantly from these. 

- The specifications and setup of the test vehicle are briefly mentioned. Please include more detailed specifications and possibly a diagram of the sensor placements on the vehicle?

 

Comments on the Quality of English Language

 Moderate editing of English language required

Author Response

Please see the attachment.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

I have no more comments

Comments on the Quality of English Language

Minor editing