# Enhancing Feature Tracking Reliability for Visual Navigation using Real-Time Safety Filter

arXiv: https://arxiv.org/abs/2502.01092 · HTML: https://mlsanity.com/paper/2502.01092/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 28 references that are in the map.
- First posted on arXiv 3 Feb 2025.

## Builds on (most cited first, 12 of 12)

- [ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras](https://mlsanity.com/paper/1610.06475/) (2016)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [Control Barrier Function Based Quadratic Programs for Safety Critical Systems](https://mlsanity.com/paper/1609.06408/) (2016)
- [The Safety Filter: A Unified View of Safety-Critical Control in Autonomous Systems](https://mlsanity.com/paper/2309.05837/) (2023)
- [PAMPC: Perception-Aware Model Predictive Control for Quadrotors](https://mlsanity.com/paper/1804.04811/) (2018)
- [Enforcing safety for vision-based controllers via Control Barrier Functions and Neural Radiance Fields](https://mlsanity.com/paper/2209.12266/) (2022)
- [Measurement-Robust Control Barrier Functions: Certainty in Safety with Uncertainty in State](https://mlsanity.com/paper/2104.14030/) (2021)
- [Observability-Aware Trajectory Optimization for Self-Calibration with Application to UAVs](https://mlsanity.com/paper/1604.07905/) (2016)
- [Safe and Robust Observer-Controller Synthesis using Control Barrier Functions](https://mlsanity.com/paper/2211.14364/) (2022)
- [Topology-Guided Path Planning for Reliable Visual Navigation of MAVs](https://mlsanity.com/paper/2107.08616/) (2021)
- [APACE: Agile and Perception-Aware Trajectory Generation for Quadrotor Flights](https://mlsanity.com/paper/2403.08365/) (2024)
- [Visibility-Constrained Control of Multirotor via Reference Governor](https://mlsanity.com/paper/2308.05334/) (2023)

## Abstract (arXiv)

Vision sensors are extensively used for localizing a robot's pose, particularly in environments where global localization tools such as GPS or motion capture systems are unavailable. In many visual navigation systems, localization is achieved by detecting and tracking visual features or landmarks, which provide information about the sensor's relative pose. For reliable feature tracking and accurate pose estimation, it is crucial to maintain visibility of a sufficient number of features. This requirement can sometimes conflict with the robot's overall task objective. In this paper, we approach it as a constrained control problem. By leveraging the invariance properties of visibility constraints within the robot's kinematic model, we propose a real-time safety filter based on quadratic programming. This filter takes a reference velocity command as input and produces a modified velocity that minimally deviates from the reference while ensuring the information score from the currently visible features remains above a user-specified threshold. Numerical simulations demonstrate that the proposed safety filter preserves the invariance condition and ensures the visibility of more features than the required minimum. We also validated its real-world performance by integrating it into a visual simultaneous localization and mapping (SLAM) algorithm, where it maintained high estimation quality in challenging environments, outperforming a simple tracking controller.