Visual Appearance Analysis of Forest Scenes for Monocular SLAM

TL;DR

This paper analyzes the challenges of monocular SLAM in forest environments, highlighting key visual differences from urban scenes and suggesting improvements for simulation and algorithm robustness.

Contribution

It compares SLAM performance in forests versus urban scenes, characterizes visual appearance differences, and offers insights for enhancing SLAM in unstructured natural environments.

Findings

SLAM struggles with complex forest terrains

Lighting changes and in-scene motion are key challenges

Simulated forests often fail to replicate real environment difficulties

Abstract

Monocular simultaneous localisation and mapping (SLAM) is a cheap and energy efficient way to enable Unmanned Aerial Vehicles (UAVs) to safely navigate managed forests and gather data crucial for monitoring tree health. SLAM research, however, has mostly been conducted in structured human environments, and as such is poorly adapted to unstructured forests. In this paper, we compare the performance of state of the art monocular SLAM systems on forest data and use visual appearance statistics to characterise the differences between forests and other environments, including a photorealistic simulated forest. We find that SLAM systems struggle with all but the most straightforward forest terrain and identify key attributes (lighting changes and in-scene motion) which distinguish forest scenes from "classic" urban datasets. These differences offer an insight into what makes forests harder to map and open the way for targeted improvements. We also demonstrate that even simulations that look impressive to the human eye can fail to properly reflect the difficult attributes of the environment they simulate, and provide suggestions for more closely mimicking natural scenes.