IOAM: A Novel Sensor Fusion-Based Wearable for Localization and Mapping

Round 1

Reviewer 1 Report

The paper is well structured and written. It provides a detailed presentation regarding an experimental evaluation of a novel SLAM methodology based on combining several approaches. While I am not an expert in all subjects covered within the paper, the methodology appears sound. That being said, I think the contribution could be improved by making the raw and processed data public using a service such as Zenodo/figshare. Furthermore, a detailed discussion regarding the threats to the study's validity and its findings, as well as a comparison in this regard against competing approaches would further strengthen the paper. A few minor observations:

• Table 1 should appear before the start of Section 5
• Perhaps Table 7 should provide more detail regarding the equipment used in each existing study, in order to allow readers to ascertain the complexity, cost and feasibility of using each of the proposed methods.

Author Response

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Author Response File: Author Response.pdf

Reviewer 2 Report

Dear Authors,

Please find the attached file for your reference. Please update the paper based on the comments and resubmit it. 

Regards 

Comments for author File: Comments.pdf

Author Response

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Author Response File: Author Response.pdf

Reviewer 3 Report

The authors present a method for indoor localization. They use one foot-mounted device on each foot. Each is equipped with an IMU and two ultrasonic range finders, one pointing inwards and one outwards. The IMU data is fused by one EKF for each foot, which is corrected using the inner ultrasound sensor and an dynamic minimum centroid distance algorithm. After that, the two estimated trajectories are fused to obtain a single trajectory for the center body of mass. Furthermore, the data of the outer ultrasound sensors is used for creating a map. The algorithm is evaluated using real data in three different scenarios.

The proposed algorithm is an interesting approach to indoor localization. The paper is well structured and easy to follow. The evaluation using real-world data is commendable. There are however some major issues:

- The state vector of the EKF should be defined at the beginning of Sect. 2.2. Furthermore, it is unclear which parts of 2.2.1 correspond to existing methods and which are new. This should be indicated explicitly.

- The authors claim to present a SLAM algorithm. However, the proposed approach corresponds to mapping with known pose: The pose is estimated independently of the map, followed by an map update with the given pose. The pose is not corrected with respect to the map, which is the key feature of SLAM.

- The novelty of the mapping algorithm is rather low and the purpose of the generated map is unclear. Since the device is used by humans, no dynamically created map is needed for obstacles avoidance or path planning. The map is also not used for localization (see previous point).

- It is hard to assess the accuracy of the map, since the provided plots are rather broad.

- It is unclear how the ground-truth for the start and end point of the trajectory is determined when calculating the RMSE. Humans may end up at a different position compared to where they started.

- A analysis of the runtime and space complexity is missing. This is particularly crucial since the proposed algorithm may run on an embedded device.

Author Response

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Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Dear Authors, 

Thank you for addressing all my comments and I don't have any further concerns.

Regards 

Author Response

We would like to express our gratitude to the reviewer for your valuable comments.

Reviewer 3 Report

With the revision, the manuscript as been improved and the majority of the concerns have been resolved to a satisfactory degree. There are however two points that requires the attention of the authors:

- Reviewer #3 comment #4: The authors refer to other literature to justify the presentation of the created maps. However, in the cited literature, classical occupancy grid maps are used, which distinguish between free, occupied and unknown. The maps used here only distinguish between free and irrelevant, and thus, another visualization would be more suited. I suggest to at least change the color of either the free grid cells or the floor plan to be able to distinguish between both in Figs. 13 and 14. Furthermore, the floor plan should be plotted above the estimated map such that it is not covered by the map itself. Finally, some close-ups of interesting areas would help to asses the mapping accuracy.

- Review #3 comment #5: The exact procedure (laser range finder and CAD layout) to determine the positions is only given in the comment to the editor. This should be added to the paper as well.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf