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Model-Based and Neural-Aided Approaches for Dog Dead Reckoning

Gal Versano, Itai Savin, Itzik Klein

TL;DR

This work proposes three algorithms for accurate positioning using only inertial sensors, collectively referred to as dog dead reckoning (DDR), and demonstrates that their neural-aided methods consistently outperform model-based approaches, achieving an absolute distance error of less than 10\%.

Abstract

Modern canine applications span medical and service roles, while robotic legged dogs serve as autonomous platforms for high-risk industrial inspection, disaster response, and search and rescue operations. For both, accurate positioning remains a significant challenge due to the cumulative drift inherent in inertial sensing. To bridge this gap, we propose three algorithms for accurate positioning using only inertial sensors, collectively referred to as dog dead reckoning (DDR). To evaluate our approaches, we designed DogMotion, a wearable unit for canine data recording. Using DogMotion, we recorded a dataset of 13 minutes. Additionally, we utilized a robotic legged dog dataset with a duration of 116 minutes. Across the two distinct datasets we demonstrate that our neural-aided methods consistently outperform model-based approaches, achieving an absolute distance error of less than 10\%. Consequently, we provide a lightweight and low-cost positioning solution for both biological and legged robotic dogs. To support reproducibility, our codebase and associated datasets have been made publicly available.

Model-Based and Neural-Aided Approaches for Dog Dead Reckoning

TL;DR

This work proposes three algorithms for accurate positioning using only inertial sensors, collectively referred to as dog dead reckoning (DDR), and demonstrates that their neural-aided methods consistently outperform model-based approaches, achieving an absolute distance error of less than 10\%.

Abstract

Modern canine applications span medical and service roles, while robotic legged dogs serve as autonomous platforms for high-risk industrial inspection, disaster response, and search and rescue operations. For both, accurate positioning remains a significant challenge due to the cumulative drift inherent in inertial sensing. To bridge this gap, we propose three algorithms for accurate positioning using only inertial sensors, collectively referred to as dog dead reckoning (DDR). To evaluate our approaches, we designed DogMotion, a wearable unit for canine data recording. Using DogMotion, we recorded a dataset of 13 minutes. Additionally, we utilized a robotic legged dog dataset with a duration of 116 minutes. Across the two distinct datasets we demonstrate that our neural-aided methods consistently outperform model-based approaches, achieving an absolute distance error of less than 10\%. Consequently, we provide a lightweight and low-cost positioning solution for both biological and legged robotic dogs. To support reproducibility, our codebase and associated datasets have been made publicly available.
Paper Structure (23 sections, 23 equations, 7 figures, 7 tables, 3 algorithms)

This paper contains 23 sections, 23 equations, 7 figures, 7 tables, 3 algorithms.

Table of Contents

  1. Introduction
  2. Problem Formulation
  3. Inertial Navigation Systems
  4. Pedestrian Dead Reckoning
  5. Proposed Approach
  6. Model-Based DDR
  7. Deep Learning-Aided DDR with ResNets
  8. Backbone Architecture
  9. Loss Functions and Training
  10. Summary
  11. Deep Learning-Aided DDR with Transformer Encoders
  12. Loss Function and Training
  13. Position update
  14. Analysis and Results
  15. Datasets
  16. ...and 8 more sections

Figures (7)

  • Figure 1: Flow diagram of our proposed model-based DDR approach.
  • Figure 2: Our proposed deep learning-aided DDR using a ResNet backbone.
  • Figure 3: Our proposed deep learning-aided DDR using a hybrid ResNet and transformer encoder backbone.
  • Figure 4: Our DogMotion device (a) Hardware connection and (b) Design and components.
  • Figure 5: The dogs equipped with our DogMotion device during field experiments.
  • ...and 2 more figures