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Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results

Pascal Schlachter, Zhibin Yu, Naveed Iqbal, Xiaofeng Wu, Sven Hinderer, Bin Yang

TL;DR

The paper tackles the challenge of reliable, low-cost indoor positioning for AMRs by introducing local reference point (LRP) assisted active radar positioning using passive LRPs. It blends geometry and fingerprinting by defining LRP fingerprints as radar-observed distances to LRPs and evaluates two positioning modalities: a precomputed look-up table and Adaptive Monte Carlo Localization (AMCL), with AMCL offering robustness to fingerprint ambiguities. A key contribution is the analysis of LRP layouts to avoid systematic and random ambiguities, showing that four LRPs of two types can yield bijective fingerprints under suitable symmetry conditions. A proof-of-concept simulation at 60 GHz demonstrates feasibility, and the discussion outlines practical considerations for multipath robustness and reflector design, paving the way for hardware trials and extended scenarios.

Abstract

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach.

Indoor Positioning based on Active Radar Sensing and Passive Reflectors: Concepts & Initial Results

TL;DR

The paper tackles the challenge of reliable, low-cost indoor positioning for AMRs by introducing local reference point (LRP) assisted active radar positioning using passive LRPs. It blends geometry and fingerprinting by defining LRP fingerprints as radar-observed distances to LRPs and evaluates two positioning modalities: a precomputed look-up table and Adaptive Monte Carlo Localization (AMCL), with AMCL offering robustness to fingerprint ambiguities. A key contribution is the analysis of LRP layouts to avoid systematic and random ambiguities, showing that four LRPs of two types can yield bijective fingerprints under suitable symmetry conditions. A proof-of-concept simulation at 60 GHz demonstrates feasibility, and the discussion outlines practical considerations for multipath robustness and reflector design, paving the way for hardware trials and extended scenarios.

Abstract

To navigate reliably in indoor environments, an industrial autonomous vehicle must know its position. However, current indoor vehicle positioning technologies either lack accuracy, usability or are too expensive. Thus, we propose a novel concept called local reference point assisted active radar positioning, which is able to overcome these drawbacks. It is based on distributing passive retroreflectors in the indoor environment such that each position of the vehicle can be identified by a unique reflection characteristic regarding the reflectors. To observe these characteristics, the autonomous vehicle is equipped with an active radar system. On one hand, this paper presents the basic idea and concept of our new approach towards indoor vehicle positioning and especially focuses on the crucial placement of the reflectors. On the other hand, it also provides a proof of concept by conducting a full system simulation including the placement of the local reference points, the radar-based distance estimation and the comparison of two different positioning methods. It successfully demonstrates the feasibility of our proposed approach.
Paper Structure (19 sections, 5 equations, 4 figures)

This paper contains 19 sections, 5 equations, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Basic Idea
  4. LRP placement
  5. Radar transmission
  6. Radar signal processing
  7. Positioning
  8. Look up table
  9. Adaptive Monte Carlo localization
  10. Placement of the Local Reference Points
  11. Systematic ambiguities
  12. Random ambiguities
  13. Proof of Concept
  14. Setup
  15. Results
  16. ...and 4 more sections

Figures (4)

  • Figure 1: Algorithm of AMCL.
  • Figure 1: (a)-(c) visualize the systematic ambiguities for exemplary LRP layouts using two, three and four LRPs of one type (black circles with crosses). Thereby, exemplary pairs of ambiguities are indicated by crosses of similar colors. (d) supports the explanation of why random ambiguities occur by showing all positions whose LRP fingerprints have the distances $d_1$ and $d_2$ in common with the fingerprint of the arbitrary considered position (red cross).
  • Figure 2: Arbitrary path of the AMR and selected LRP layout used for the system simulation. The colored areas indicate positions with the same LRP fingerprint. In the white areas, all positions have a unique LRP fingerprint.
  • Figure 3: Errors between the estimated and true positions for all samples of the exemplary path using the look up table and AMCL, respectively. Thereby, for AMCL the mean and standard deviation of ten runs is reported. In contrast, the look up table approach is deterministic.