Markerless Aerial-Terrestrial Co-Registration of Forest Point Clouds using a Deformable Pose Graph

For biodiversity and forestry applications, end-users desire maps of forests that are fully detailed, from the forest floor to the canopy. Terrestrial laser scanning and aerial laser scanning are accurate and increasingly mature methods for scanning the forest. However, individually they are not able to estimate attributes such as tree height, trunk diameter and canopy density due to the inherent differences in their field-of-view and mapping processes. In this work, we present a pipeline that can automatically generate a single joint terrestrial and aerial forest reconstruction. The novelty of the approach is a marker-free registration pipeline, which estimates a set of relative transformation constraints between the aerial cloud and terrestrial sub-clouds without requiring any co-registration reflective markers to be physically placed in the scene. Our method then uses these constraints in a pose graph formulation, which enables us to finely align the respective clouds while respecting spatial constraints introduced by the terrestrial SLAM scanning process. We demonstrate that our approach can produce a fine-grained and complete reconstruction of large-scale natural environments, enabling multi-platform data capture for forestry applications without requiring external infrastructure.

Autonomous Forest Inventory with Legged Robots: System Design and Field Deployment

We present a solution for autonomous forest inventory with a legged robotic platform. Compared to their wheeled and aerial counterparts, legged platforms offer an attractive balance of endurance and low soil impact for forest applications. In this paper, we present the complete system architecture of our forest inventory solution which includes state estimation, navigation, mission planning, and real-time tree segmentation and trait estimation. We present preliminary results for three campaigns in forests in Finland and the UK and summarize the main outcomes, lessons, and challenges. Our UK experiment at the Forest of Dean with the ANYmal D legged platform, achieved an autonomous survey of a 0.96 hectare plot in 20 min, identifying over 100 trees with typical DBH accuracy of 2 cm.

Team CERBERUS Wins the DARPA Subterranean Challenge: Technical Overview and Lessons Learned

This article presents the CERBERUS robotic system-of-systems, which won the DARPA Subterranean Challenge Final Event in 2021. The Subterranean Challenge was organized by DARPA with the vision to facilitate the novel technologies necessary to reliably explore diverse underground environments despite the grueling challenges they present for robotic autonomy. Due to their geometric complexity, degraded perceptual conditions combined with lack of GPS support, austere navigation conditions, and denied communications, subterranean settings render autonomous operations particularly demanding. In response to this challenge, we developed the CERBERUS system which exploits the synergy of legged and flying robots, coupled with robust control especially for overcoming perilous terrain, multi-modal and multi-robot perception for localization and mapping in conditions of sensor degradation, and resilient autonomy through unified exploration path planning and local motion planning that reflects robot-specific limitations. Based on its ability to explore diverse underground environments and its high-level command and control by a single human supervisor, CERBERUS demonstrated efficient exploration, reliable detection of objects of interest, and accurate mapping. In this article, we report results from both the preliminary runs and the final Prize Round of the DARPA Subterranean Challenge, and discuss highlights and challenges faced, alongside lessons learned for the benefit of the community.