# GPS-denied Navigation: Attitude, Position, Linear Velocity, and Gravity Estimation with Nonlinear Stochastic Observer

arXiv: https://arxiv.org/abs/2104.09920 · HTML: https://mlsanity.com/paper/2104.09920/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Apr 2021.

## Built on by (most cited first, 3 of 3)

- [MoCom: Motion-based Inter-MAV Visual Communication Using Event Vision and Spiking Neural Networks](https://mlsanity.com/paper/2510.14770/) (2025)
- [MAVR-Net: Robust Multi-View Learning for MAV Action Recognition with Cross-View Attention](https://mlsanity.com/paper/2510.15448/) (2025)
- [MobiAct: Efficient MAV Action Recognition Using MobileNetV4 with Contrastive Learning and Knowledge Distillation](https://mlsanity.com/paper/2510.19273/) (2025)

## Abstract (arXiv)

Successful navigation of a rigid-body traveling with six degrees of freedom (6 DoF) requires accurate estimation of attitude , position, and linear velocity. The true navigation dynamics are highly nonlinear and are modeled on the matrix Lie group of SE2(3). This paper presents novel geometric nonlinear continuous stochastic navigation observers on SE2(3) capturing the true nonlinearity of the problem. The proposed observers combines IMU and landmark measurements. It efficiently handles the IMU measurement noise. The proposed observers are guaranteed to be almost semi-globally uniformly ultimately bounded in the mean square. Quaternion representation is provided. A real-world quadrotor measurement dataset is used to validate the effectiveness of the proposed observers in its discrete form. Keywords: Inertial navigation, stochastic system, Brownian motion process, stochastic filter algorithm, stochastic differential equation, Lie group, SE(3), SO(3), pose estimator, position, attitude, feature measurement, inertial measurement unit, IMU.