Autonomous Under Canopy Navigation & Mapping in Forests (DigiForest @ Stein am Rhein, Switzerland) @autonomousrobotslab

Uploaded July 2024 | Updated September 2026, 3 weeks ago

In this video we present results of our lab from the latest field deployments conducted in the scope of the Digiforest EU project, in Stein am Rhein, Switzerland.

Digiforest brings together various partners working on aerial and legged robots, autonomous harvesters, and forestry decision-makers. The goal of the project is to enable autonomous robot navigation, exploration, and mapping, both below and above the canopy, to create a data pipeline that can support and enhance foresters' decision-making systems.

Autonomous Under Canopy Navigation & Mapping in Forests (DigiForest @ Stein am Rhein, Switzerland)

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$Resource-aware Online Parameter Adaptation for Computationally-constrained Visual-Inertial Odometry In this work, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computationally expensive processes. To achieve this objective, the algorithm proposes selected changes in the vision front-end and optimization back-end of visual-inertial odometry algorithms, both prior to execution and in real time based on an online profiling of available resources. The method also utilizes information from the motion dynamics experienced by the system to manipulate parameters online. The general policy is demonstrated on three established algorithms, namely S-MSCKF, VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset. The proposed approach achieved comparable performance at a fraction of the original computational cost.$