# Vision-Aided Absolute Trajectory Estimation Using an Unsupervised Deep Network with Online Error Correction

arXiv: https://arxiv.org/abs/1803.05850 · HTML: https://mlsanity.com/paper/1803.05850/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Efficient Deep Visual and Inertial Odometry with Adaptive Visual Modality Selection](https://mlsanity.com/paper/2205.06187/) (2022)
- [Causal Transformer for Fusion and Pose Estimation in Deep Visual Inertial Odometry](https://mlsanity.com/paper/2409.08769/) (2024)
- [OdoNet: Untethered Speed Aiding for Vehicle Navigation Without Hardware Wheeled Odometer](https://mlsanity.com/paper/2109.03091/) (2021)
- [DAP-Pose: Deep Temporal Alignment and Physics-aware Cross-modal Sensor Fusion for Robust Pose Estimation](https://mlsanity.com/paper/2607.23755/) (2026)

## Abstract (arXiv)

We present an unsupervised deep neural network approach to the fusion of RGB-D imagery with inertial measurements for absolute trajectory estimation. Our network, dubbed the Visual-Inertial-Odometry Learner (VIOLearner), learns to perform visual-inertial odometry (VIO) without inertial measurement unit (IMU) intrinsic parameters (corresponding to gyroscope and accelerometer bias or white noise) or the extrinsic calibration between an IMU and camera. The network learns to integrate IMU measurements and generate hypothesis trajectories which are then corrected online according to the Jacobians of scaled image projection errors with respect to a spatial grid of pixel coordinates. We evaluate our network against state-of-the-art (SOA) visual-inertial odometry, visual odometry, and visual simultaneous localization and mapping (VSLAM) approaches on the KITTI Odometry dataset and demonstrate competitive odometry performance.