# Robust Active Visual Perching with Quadrotors on Inclined Surfaces

arXiv: https://arxiv.org/abs/2204.02458 · HTML: https://mlsanity.com/paper/2204.02458/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Apr 2022.

## Built on by (most cited first, 7 of 7)

- [Inverted Landing in a Small Aerial Robot via Deep Reinforcement Learning for Triggering and Control of Rotational Maneuvers](https://mlsanity.com/paper/2209.11043/) (2022)
- [Visual Environment Assessment for Safe Autonomous Quadrotor Landing](https://mlsanity.com/paper/2311.10065/) (2023)
- [From Flies to Robots: Inverted Landing in Small Quadcopters with Dynamic Perching](https://mlsanity.com/paper/2403.00128/) (2024)
- [A Vision-based Autonomous Perching Approach for Nano Aerial Vehicles](https://mlsanity.com/paper/2306.09591/) (2023)
- [From Ceilings to Walls: Universal Dynamic Perching of Small Aerial Robots on Surfaces with Variable Orientations](https://mlsanity.com/paper/2412.19765/) (2024)
- [HUNT: High-Speed UAV Navigation and Tracking in Unstructured Environments via Instantaneous Relative Frames](https://mlsanity.com/paper/2509.19452/) (2025)
- [PerchRL: Vision-Based Agile Perching on Inclined Platforms under Rapid and Irregular Motion](https://mlsanity.com/paper/2606.03441/) (2026)

## Abstract (arXiv)

Autonomous Micro Aerial Vehicles are deployed for a variety tasks including surveillance and monitoring. Perching and staring allow the vehicle to monitor targets without flying, saving battery power and increasing the overall mission time without the need to frequently replace batteries. This paper addresses the Active Visual Perching (AVP) control problem to autonomously perch on inclined surfaces up to 90^circ. Our approach generates dynamically feasible trajectories to navigate and perch on a desired target location, while taking into account actuator and Field of View (FoV) constraints. By replanning in mid-flight, we take advantage of more accurate target localization increasing the perching maneuver's robustness to target localization or control errors. We leverage the Karush-Kuhn-Tucker (KKT) conditions to identify the compatibility between planning objectives and the visual sensing constraint during the planned maneuver. Furthermore, we experimentally identify the corresponding boundary conditions that maximizes the spatio-temporal target visibility during the perching maneuver. The proposed approach works on-board in real-time with significant computational constraints relying exclusively on cameras and an Inertial Measurement Unit (IMU). Experimental results validate the proposed approach and shows the higher success rate as well as increased target interception precision and accuracy with respect to a one-shot planning approach, while still retaining aggressive capabilities with flight envelopes that include large excursions from the hover position on inclined surfaces up to 90^circ, angular speeds up to 750~deg/s, and accelerations up to 10~m/s2.