R-LIO: Rotating Lidar Inertial Odometry and Mapping

Round 1

Reviewer 1 Report

In the paper, the authors proposed a novel SLAM algorithm, R-LIO, to achieve real-time and high-precision localization and map building. The experimental results showed R-LIO achieve better localization and mapping accuracy in rotating lidar data. And R-LIO has the competitive localization accuracy as LIO-SAM, FAST-LIO2 and Faster-LIO. Beside the four public datasets,  the three different private datasets are used in the experimnets.  

1.There are some problems with the format of the formula in the paper, please modify them.

2.The authors should describe the accuracy of the real trajectory.

3. Could the authors supply some experiments in the outside scenarios with fewer landmarks?

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

This paper proposes a SLAM method which includes steps to remove distortions due to LiDAR movement, incorporate inertial measurements to improve registration, register consecutive point clouds through feature matching, and finally apply loop detection.

     1)     The first improvement is indicated as enabling the rotation of the LiDAR. LOAM is not lidar specific and works with rotating and non-rotating Lidars.

     2)     The second improvement is denoted as distortion compensation. This distortion is related to the fact that LiDAR is moving while capturing data. This technique has been known in the field and has been applied for distortion removal from the LiDAR data for a long time. This step is so commonly accepted in the field as the first logical step in processing the LiDAR data that some public datasets like the KITTI LiDAR odometry dataset have already gone through distortion removal to make the processing easier for the research community and help them focus on other more important aspects.

     3) Loop detection and closure is again another commonly known technique. LOAM does not include loop closure on purpose to show that even without loop closure and the associated corrections the method is capable of achieving high accuracy. The focus of LOAM was on reducing drift purely based on PC registration and without any help from IMU or loop closure, etc. So the comparisons made with LOAM throughout the paper are not accurate.

     4)     Application of IMU as part of a SLAM system is not new either. This area is actually pretty advanced and has been the topic of research for a long time.

     5)  There are public datasets such as KITTI with benchmarks where researcher can compare the accuracy of their results. No comparison to available benchmarks are made in this paper.

     In general, there is no aspect of this research work that can be considered novel in any form.      

 

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The overall idea is good and the experiments are nice but the paper itself is a bit messy and seems rushed to be published.

· Acronyms must be defined at least on first use. GNSS, SLAM, etc

· Quality of figure 5 is low. Lines are deformed.

· LOAM is not cited in line 38

· English writting must be improved.

· The paper has not been grammatically checked and seems rushed. Revise the full text. Some examples: 

adaptive gird for example, in line 188

found instead of find, in line 193

You will find more grammar problems, please perform a full check.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

The fact that KITTI dataset is collected in 2012 does not mean that the data has a problem or that it cannot be used as a benchmark. It is still being used by the community and is considered a very good benchmark. (see the following for example)

Recalibrating the KITTI dataset camera setup for improved odometry accuracy

 I think it would still be very helpful to see how the proposed algorithm performs on this data specially that most of the comparison are made to LOAM which is one of the top performers on the KITTI dataset. What is holding the authors back from trying their algorithm on the KITTI dataset? I think including the results on KITTI data will add to the value of this paper and also makes the comparisons fair. 

Author Response

Please see the attachment.

Author Response File: Author Response.pdf