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.

Scientific Exploration of Challenging Planetary Analog Environments with a Team of Legged Robots

The interest in exploring planetary bodies for scientific investigation and in-situ resource utilization is ever-rising. Yet, many sites of interest are inaccessible to state-of-the-art planetary exploration robots because of the robots' inability to traverse steep slopes, unstructured terrain, and loose soil. Additionally, current single-robot approaches only allow a limited exploration speed and a single set of skills. Here, we present a team of legged robots with complementary skills for exploration missions in challenging planetary analog environments. We equipped the robots with an efficient locomotion controller, a mapping pipeline for online and post-mission visualization, instance segmentation to highlight scientific targets, and scientific instruments for remote and in-situ investigation. Furthermore, we integrated a robotic arm on one of the robots to enable high-precision measurements. Legged robots can swiftly navigate representative terrains, such as granular slopes beyond 25 degrees, loose soil, and unstructured terrain, highlighting their advantages compared to wheeled rover systems. We successfully verified the approach in analog deployments at the BeyondGravity ExoMars rover testbed, in a quarry in Switzerland, and at the Space Resources Challenge in Luxembourg. Our results show that a team of legged robots with advanced locomotion, perception, and measurement skills, as well as task-level autonomy, can conduct successful, effective missions in a short time. Our approach enables the scientific exploration of planetary target sites that are currently out of human and robotic reach.

X-ICP: Localizability-Aware LiDAR Registration for Robust Localization in Extreme Environments

Modern robotic systems are required to operate in challenging environments, which demand reliable localization under challenging conditions. LiDAR-based localization methods, such as the Iterative Closest Point (ICP) algorithm, can suffer in geometrically uninformative environments that are known to deteriorate registration performance and push optimization toward divergence along weakly constrained directions. To overcome this issue, this work proposes i) a robust multi-category (non-)localizability detection module, and ii) a localizability-aware constrained ICP optimization module and couples both in a unified manner. The proposed localizability detection is achieved by utilizing the correspondences between the scan and the map to analyze the alignment strength against the principal directions of the optimization as part of its multi-category LiDAR localizability analysis. In the second part, this localizability analysis is then tightly integrated into the scan-to-map point cloud registration to generate drift-free pose updates along well-constrained directions. The proposed method is thoroughly evaluated and compared to state-of-the-art methods in simulation and during real-world experiments, underlying the gain in performance and reliability in LiDAR-challenging scenarios. In all experiments, the proposed framework demonstrates accurate and generalizable localizability detection and robust pose estimation without environment-specific parameter tuning.