# Where Am I Now? Dynamically Finding Optimal Sensor States to Minimize Localization Uncertainty for a Perception-Denied Rover

arXiv: https://arxiv.org/abs/2211.16721 · HTML: https://mlsanity.com/paper/2211.16721/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 41 references that are in the map.
- First posted on arXiv 30 Nov 2022.

## Builds on (most cited first, 9 of 9)

- [RADIATE: A Radar Dataset for Automotive Perception in Bad Weather](https://mlsanity.com/paper/2010.09076/) (2020)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontiers](https://mlsanity.com/paper/2207.00254/) (2022)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [Weather Influence and Classification with Automotive Lidar Sensors](https://mlsanity.com/paper/1906.07675/) (2019)
- [AirCapRL: Autonomous Aerial Human Motion Capture using Deep Reinforcement Learning](https://mlsanity.com/paper/2007.06343/) (2020)
- [Active Perception based Formation Control for Multiple Aerial Vehicles](https://mlsanity.com/paper/1901.07813/) (2019)
- [Deep Neural Network-based Cooperative Visual Tracking through Multiple Micro Aerial Vehicles](https://mlsanity.com/paper/1802.01346/) (2018)
- [Multi-Modal Active Perception for Information Gathering in Science Missions](https://mlsanity.com/paper/1712.09716/) (2017)

## Built on by (most cited first, 1 of 1)

- [When to Localize? A Risk-Constrained Reinforcement Learning Approach](https://mlsanity.com/paper/2411.02788/) (2024)

## Abstract (arXiv)

We present DyFOS, an active perception method that dynamically finds optimal states to minimize localization uncertainty while avoiding obstacles and occlusions. We consider the scenario where a perception-denied rover relies on position and uncertainty measurements from a viewer robot to localize itself along an obstacle-filled path. The position uncertainty from the viewer's sensor is a function of the states of the sensor itself, the rover, and the surrounding environment. To find an optimal sensor state that minimizes the rover's localization uncertainty, DyFOS uses a localization uncertainty prediction pipeline in an optimization search. Given numerous samples of the states mentioned above, the pipeline predicts the rover's localization uncertainty with the help of a trained, complex state-dependent sensor measurement model (a probabilistic neural network). Our pipeline also predicts occlusion and obstacle collision to remove undesirable viewer states and reduce unnecessary computations. We evaluate the proposed method numerically and in simulation. Our results show that DyFOS is faster than brute force yet performs on par. DyFOS also yielded lower localization uncertainties than faster random and heuristic-based searches.