Finding a Landing Site on an Urban Area: A Multi-Resolution Probabilistic Approach

TL;DR

This paper presents a multi-resolution probabilistic method for autonomous drone landing site detection in urban environments, balancing exploration speed and resolution through a hierarchical approach and probabilistic decision-making.

Contribution

It introduces a novel multi-resolution approach combining semantic segmentation and probabilistic updates for efficient urban landing site detection.

Findings

Effective in dense urban scenarios

Achieves high confidence in landing site identification

Demonstrated with realistic simulation examples

Abstract

This paper considers the problem of finding a landing spot for a drone in a dense urban environment. The conflicting requirement of fast exploration and high resolution is solved using a multi-resolution approach, by which visual information is collected by the drone at decreasing altitudes so that spatial resolution of the acquired images increases monotonically. A probability distribution is used to capture the uncertainty of the decision process for each terrain patch. The distributions are updated as information from different altitudes is collected. When the confidence level for one of the patches becomes larger than a pre-specified threshold, suitability for landing is declared. One of the main building blocks of the approach is a semantic segmentation algorithm that attaches probabilities to each pixel of a single view. The decision algorithm combines these probabilities with a priori data and previous measurements to obtain the best estimates. Feasibility is illustrated by presenting a number of examples generated by a realistic closed-loop simulator.