# IDOL: Inertial Deep Orientation-Estimation and Localization arXiv: https://arxiv.org/abs/2102.04024 ยท HTML: https://mlsanity.com/paper/2102.04024/ ## Key facts - Cited by 26 papers in the map (data through 14 Oct 2026). - Builds on 15 of its 28 references that are in the map. - First posted on arXiv 8 Feb 2021. ## Builds on (most cited first, 15 of 15) - [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017) - [The TUM VI Benchmark for Evaluating Visual-Inertial Odometry](https://mlsanity.com/paper/1804.06120/) (2018) - [RoNIN: Robust Neural Inertial Navigation in the Wild: Benchmark, Evaluations, and New Methods](https://mlsanity.com/paper/1905.12853/) (2019) - [IONet: Learning to Cure the Curse of Drift in Inertial Odometry](https://mlsanity.com/paper/1802.02209/) (2018) - [TLIO: Tight Learned Inertial Odometry](https://mlsanity.com/paper/2007.01867/) (2020) - [RIDI: Robust IMU Double Integration](https://mlsanity.com/paper/1712.09004/) (2017) - [Multivariate Uncertainty in Deep Learning](https://mlsanity.com/paper/1910.14215/) (2019) - [Denoising IMU Gyroscopes with Deep Learning for Open-Loop Attitude Estimation](https://mlsanity.com/paper/2002.10718/) (2020) - [ADVIO: An authentic dataset for visual-inertial odometry](https://mlsanity.com/paper/1807.09828/) (2018) - [OxIOD: The Dataset for Deep Inertial Odometry](https://mlsanity.com/paper/1809.07491/) (2018) - [Inertial Odometry on Handheld Smartphones](https://mlsanity.com/paper/1703.00154/) (2017) - [Deep Learning Based Speed Estimation for Constraining Strapdown Inertial Navigation on Smartphones](https://mlsanity.com/paper/1808.03485/) (2018) - [Magnetometer calibration using inertial sensors](https://mlsanity.com/paper/1601.05257/) (2016) ## Built on by (most cited first, 26 of 26) - [Deep Learning for Inertial Positioning: A Survey](https://mlsanity.com/paper/2303.03757/) (2023) - [RIO: Rotation-equivariance supervised learning of robust inertial odometry](https://mlsanity.com/paper/2111.11676/) (2021) - [Neural Inertial Localization](https://mlsanity.com/paper/2203.15851/) (2022) - [CTIN: Robust Contextual Transformer Network for Inertial Navigation](https://mlsanity.com/paper/2112.02143/) (2021) - [EqNIO: Subequivariant Neural Inertial Odometry](https://mlsanity.com/paper/2408.06321/) (2024) - [AirIO: Learning Inertial Odometry with Enhanced IMU Feature Observability](https://mlsanity.com/paper/2501.15659/) (2025) - [Recent Advancements in Deep Learning Applications and Methods for Autonomous Navigation: A Comprehensive Review](https://mlsanity.com/paper/2302.11089/) (2023) - [AirIMU: Learning Uncertainty Propagation for Inertial Odometry](https://mlsanity.com/paper/2310.04874/) (2023) - [Generalizable End-to-End Deep Learning Frameworks for Real-Time Attitude Estimation Using 6DoF Inertial Measurement Units](https://mlsanity.com/paper/2302.06037/) (2023) - [Legged Robot State Estimation With Invariant Extended Kalman Filter Using Neural Measurement Network](https://mlsanity.com/paper/2402.00366/) (2024) - [RetailOpt: Opt-In, Easy-to-Deploy Trajectory Estimation from Smartphone Motion Data and Retail Facility Information](https://mlsanity.com/paper/2404.12548/) (2024) - [Learning IMU Bias with Diffusion Model](https://mlsanity.com/paper/2505.11763/) (2025) - [Learnable Spatio-Temporal Map Embeddings for Deep Inertial Localization](https://mlsanity.com/paper/2211.07635/) (2022) - [FusionLoc: Camera-2D LiDAR Fusion Using Multi-Head Self-Attention for End-to-End Serving Robot Relocalization](https://mlsanity.com/paper/2303.06872/) (2023) - [Physics-Informed Data Denoising for Real-Life Sensing Systems](https://mlsanity.com/paper/2311.06968/) (2023) - [Model-Driven Deep Neural Network for Enhanced AoA Estimation Using 5G gNB](https://mlsanity.com/paper/2501.00009/) (2024) - [Egocentric Action-aware Inertial Localization in Point Clouds with Vision-Language Guidance](https://mlsanity.com/paper/2505.14346/) (2025) - [RIOT: Recursive Inertial Odometry Transformer for Localisation from Low-Cost IMU Measurements](https://mlsanity.com/paper/2303.01641/) (2023) - [An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving](https://mlsanity.com/paper/2502.16389/) (2025) - [A Plug-and-Play Learning-based IMU Bias Factor for Robust Visual-Inertial Odometry](https://mlsanity.com/paper/2503.12527/) (2025) - [Experience Paper: Adopting Activity Recognition in On-demand Food Delivery Business](https://mlsanity.com/paper/2509.24303/) (2025) - [Piggyback Camera: Easy-to-Deploy Visual Surveillance by Mobile Sensing on Commercial Robot Vacuums](https://mlsanity.com/paper/2507.04910/) (2025) - [UMLoc: Uncertainty-Aware Map-Constrained Inertial Localization with Quantified Bounds](https://mlsanity.com/paper/2601.06602/) (2026) - [Tracking Large-scale Shared Bikes with Inertial Motion Learning in GNSS Blocked Environments](https://mlsanity.com/paper/2605.07412/) (2026) - [MARIO: Motion-Augmented Real-Time Multi-Sensor Inertial Odometry](https://mlsanity.com/paper/2606.02996/) (2026) - [GINIO: A Geometric SO(3)-Equivariant Interface for Neural Inertial Odometry](https://mlsanity.com/paper/2609.25338/) (2026) ## Abstract (arXiv) Many smartphone applications use inertial measurement units (IMUs) to sense movement, but the use of these sensors for pedestrian localization can be challenging due to their noise characteristics. Recent data-driven inertial odometry approaches have demonstrated the increasing feasibility of inertial navigation. However, they still rely upon conventional smartphone orientation estimates that they assume to be accurate, while in fact these orientation estimates can be a significant source of error. To address the problem of inaccurate orientation estimates, we present a two-stage, data-driven pipeline using a commodity smartphone that first estimates device orientations and then estimates device position. The orientation module relies on a recurrent neural network and Extended Kalman Filter to obtain orientation estimates that are used to then rotate raw IMU measurements into the appropriate reference frame. The position module then passes those measurements through another recurrent network architecture to perform localization. Our proposed method outperforms state-of-the-art methods in both orientation and position error on a large dataset we constructed that contains 20 hours of pedestrian motion across 3 buildings and 15 subjects.