PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms

• URL: http://arxiv.org/abs/2601.03040v1
• Date: Tue, 06 Jan 2026 14:19:50 GMT
• Title: PiDR: Physics-Informed Inertial Dead Reckoning for Autonomous Platforms
• Authors: Arup Kumar Sahoo, Itzik Klein,
• Abstract summary: We propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms.<n>We evaluate PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle.
• Score: 5.5217350574838875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: A fundamental requirement for full autonomy is the ability to sustain accurate navigation in the absence of external data, such as GNSS signals or visual information. In these challenging environments, the platform must rely exclusively on inertial sensors, leading to pure inertial navigation. However, the inherent noise and other error terms of the inertial sensors in such real-world scenarios will cause the navigation solution to drift over time. Although conventional deep-learning models have emerged as a possible approach to inertial navigation, they are inherently black-box in nature. Furthermore, they struggle to learn effectively with limited supervised sensor data and often fail to preserve physical principles. To address these limitations, we propose PiDR, a physics-informed inertial dead-reckoning framework for autonomous platforms in situations of pure inertial navigation. PiDR offers transparency by explicitly integrating inertial navigation principles into the network training process through the physics-informed residual component. PiDR plays a crucial role in mitigating abrupt trajectory deviations even under limited or sparse supervision. We evaluated PiDR on real-world datasets collected by a mobile robot and an autonomous underwater vehicle. We obtained more than 29% positioning improvement in both datasets, demonstrating the ability of PiDR to generalize different platforms operating in various environments and dynamics. Thus, PiDR offers a robust, lightweight, yet effective architecture and can be deployed on resource-constrained platforms, enabling real-time pure inertial navigation in adverse scenarios.

Related papers

• ZTRS: Zero-Imitation End-to-end Autonomous Driving with Trajectory Scoring [52.195295396336526]
  ZTRS (Zero-Imitation End-to-End Autonomous Driving with Trajectory Scoring) is a framework that combines the strengths of both worlds: sensor inputs without losing information and RL training for robust planning.<n>ZTRS demonstrates strong performance across three benchmarks: Navtest, Navhard, and HUGSIM.
  arXiv  Detail & Related papers  (2025-10-28T06:26:36Z)
• Forecasting Continuous Non-Conservative Dynamical Systems in SO(3) [51.510040541600176]
  We propose a novel approach to modeling the rotation of moving objects in computer vision.<n>Our approach is agnostic to energy and momentum conservation while being robust to input noise.<n>By learning to approximate object dynamics from noisy states during training, our model attains robust extrapolation capabilities in simulation and various real-world settings.
  arXiv  Detail & Related papers  (2025-08-11T09:03:10Z)
• MoRPI-PINN: A Physics-Informed Framework for Mobile Robot Pure Inertial Navigation [5.5217350574838875]
  We propose MoRPI-PINN as a physics-informed neural network framework for accurate inertial-based mobile robot navigation.<n>Using real-world experiments, we show accuracy improvements of over 85% compared to other approaches.
  arXiv  Detail & Related papers  (2025-07-24T09:02:13Z)
• WMINet: A Wheel-Mounted Inertial Learning Approach For Mobile-Robot Positioning [2.915868985330569]
  We propose WMINet a wheel-mounted inertial deep learning approach to estimate the mobile robot's position based only on its inertial sensors.<n>To that end, we merge two common practical methods to reduce inertial drift: a wheel-mounted approach and driving the mobile robot in periodic trajectories.<n>Our approach demonstrated a 66% improvement over state-of-the-art approaches.
  arXiv  Detail & Related papers  (2025-03-17T10:43:46Z)
• ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments [56.194988818341976]
  Vision-language navigation is a task that requires an agent to follow instructions to navigate in environments. We propose ETPNav, which focuses on two critical skills: 1) the capability to abstract environments and generate long-range navigation plans, and 2) the ability of obstacle-avoiding control in continuous environments. ETPNav yields more than 10% and 20% improvements over prior state-of-the-art on R2R-CE and RxR-CE datasets.
  arXiv  Detail & Related papers  (2023-04-06T13:07:17Z)
• Towards Generalisable Deep Inertial Tracking via Geometry-Aware Learning [2.694262942445446]
  Inertial tracking plays a key role under momentary unfavourable operational conditions. Inertial tracking has traditionally (i) suffered from excessive error growth and (ii) required extensive and cumbersome tuning. We present DIT: a novel Deep learning Inertial Tracking system that overcomes prior limitations.
  arXiv  Detail & Related papers  (2021-06-29T08:50:23Z)
• Efficient and Robust LiDAR-Based End-to-End Navigation [132.52661670308606]
  We present an efficient and robust LiDAR-based end-to-end navigation framework. We propose Fast-LiDARNet that is based on sparse convolution kernel optimization and hardware-aware model design. We then propose Hybrid Evidential Fusion that directly estimates the uncertainty of the prediction from only a single forward pass.
  arXiv  Detail & Related papers  (2021-05-20T17:52:37Z)
• Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions [7.514178230130502]
  We propose a framework for resilient autonomous computation in perceptually challenging environments with mobility-stressing elements. We propose a fast settling algorithm to generate robust multi-fidelity traversability estimates in real-time. The proposed approach was deployed on multiple physical systems including skid-steer and tracked robots, a high-speed RC car and legged robots.
  arXiv  Detail & Related papers  (2021-01-26T22:13:01Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference [49.88536971774444]
  Inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots. Exploiting inertial data for accurate and reliable pedestrian navigation supports is a key component for emerging Internet-of-Things applications and services. We present and release the Oxford Inertial Odometry dataset (OxIOD), a first-of-its-kind public dataset for deep learning based inertial navigation research.
  arXiv  Detail & Related papers  (2020-01-13T04:41:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.