AgriColMap: Aerial-Ground Collaborative 3D Mapping for Precision Farming

TL;DR

AgriColMap introduces a novel multimodal map registration method combining aerial and ground data for improved precision farming, effectively handling large misalignments and differences in map resolutions.

Contribution

The paper presents AgriColMap, a new map registration pipeline that uses multimodal environment representation and optical flow for robust aerial-ground map alignment in agriculture.

Findings

Outperforms state-of-the-art registration techniques

Handles large initial misalignments effectively

Validated on real-world agricultural datasets

Abstract

The combination of aerial survey capabilities of Unmanned Aerial Vehicles with targeted intervention abilities of agricultural Unmanned Ground Vehicles can significantly improve the effectiveness of robotic systems applied to precision agriculture. In this context, building and updating a common map of the field is an essential but challenging task. The maps built using robots of different types show differences in size, resolution and scale, the associated geolocation data may be inaccurate and biased, while the repetitiveness of both visual appearance and geometric structures found within agricultural contexts render classical map merging techniques ineffective. In this paper we propose AgriColMap, a novel map registration pipeline that leverages a grid-based multimodal environment representation which includes a vegetation index map and a Digital Surface Model. We cast the data association problem between maps built from UAVs and UGVs as a multimodal, large displacement dense optical flow estimation. The dominant, coherent flows, selected using a voting scheme, are used as point-to-point correspondences to infer a preliminary non-rigid alignment between the maps. A final refinement is then performed, by exploiting only meaningful parts of the registered maps. We evaluate our system using real world data for 3 fields with different crop species. The results show that our method outperforms several state of the art map registration and matching techniques by a large margin, and has a higher tolerance to large initial misalignments. We release an implementation of the proposed approach along with the acquired datasets with this paper.