Depth Completion in Unseen Field Robotics Environments Using Extremely Sparse Depth Measurements

Autonomous field robots operating in unstructured environments require robust perception to ensure safe and reliable operations. Recent advances in monocular depth estimation have demonstrated the potential of low-cost cameras as depth sensors; however, their adoption in field robotics remains limited due to the absence of reliable scale cues, ambiguous or low-texture conditions, and the scarcity of large-scale datasets. To address these challenges, we propose a depth completion model that trains on synthetic data and uses extremely sparse measurements from depth sensors to predict dense metric depth in unseen field robotics environments. A synthetic dataset generation pipeline tailored to field robotics enables the creation of multiple realistic datasets for training purposes. This dataset generation approach utilizes textured 3D meshes from Structure from Motion and photorealistic rendering with novel viewpoint synthesis to simulate diverse field robotics scenarios. Our approach achieves an end-to-end latency of 53 ms per frame on a Nvidia Jetson AGX Orin, enabling real-time deployment on embedded platforms. Extensive evaluation demonstrates competitive performance across diverse real-world field robotics scenarios.

Discontinuity-aware Normal Integration for Generic Central Camera Models

Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-shading and photometric stereo. The vast majority of existing approaches for normal integration handle only implicitly the presence of depth discontinuities and are limited to orthographic or ideal pinhole cameras. In this paper, we propose a novel formulation that allows modeling discontinuities explicitly and handling generic central cameras. Our key idea is based on a local planarity assumption, that we model through constraints between surface normals and ray directions. Compared to existing methods, our approach more accurately approximates the relation between depth and surface normals, achieves state-of-the-art results on the standard normal integration benchmark, and is the first to directly handle generic central camera models.

CompSLAM: Complementary Hierarchical Multi-Modal Localization and Mapping for Robot Autonomy in Underground Environments

Robot autonomy in unknown, GPS-denied, and complex underground environments requires real-time, robust, and accurate onboard pose estimation and mapping for reliable operations. This becomes particularly challenging in perception-degraded subterranean conditions under harsh environmental factors, including darkness, dust, and geometrically self-similar structures. This paper details CompSLAM, a highly resilient and hierarchical multi-modal localization and mapping framework designed to address these challenges. Its flexible architecture achieves resilience through redundancy by leveraging the complementary nature of pose estimates derived from diverse sensor modalities. Developed during the DARPA Subterranean Challenge, CompSLAM was successfully deployed on all aerial, legged, and wheeled robots of Team Cerberus during their competition-winning final run. Furthermore, it has proven to be a reliable odometry and mapping solution in various subsequent projects, with extensions enabling multi-robot map sharing for marsupial robotic deployments and collaborative mapping. This paper also introduces a comprehensive dataset acquired by a manually teleoperated quadrupedal robot, covering a significant portion of the DARPA Subterranean Challenge finals course. This dataset evaluates CompSLAM's robustness to sensor degradations as the robot traverses 740 meters in an environment characterized by highly variable geometries and demanding lighting conditions. The CompSLAM code and the DARPA SubT Finals dataset are made publicly available for the benefit of the robotics community

Towards Open-Source and Modular Space Systems with ATMOS

In the near future, autonomous space systems will compose a large number of the spacecraft being deployed. Their tasks will involve autonomous rendezvous and proximity operations with large structures, such as inspections or assembly of orbiting space stations and maintenance and human-assistance tasks over shared workspaces. To promote replicable and reliable scientific results for autonomous control of spacecraft, we present the design of a space systems laboratory based on open-source and modular software and hardware. The simulation software provides a software-in-the-loop (SITL) architecture that seamlessly transfers simulated results to the ATMOS platforms, developed for testing of multi-agent autonomy schemes for microgravity. The manuscript presents the KTH space systems laboratory facilities and the ATMOS platform as open-source hardware and software contributions. Preliminary results showcase SITL and real testing.

Learning Affordances from Interactive Exploration using an Object-level Map

Many robotic tasks in real-world environments require physical interactions with an object such as pick up or push. For successful interactions, the robot needs to know the object's affordances, which are defined as the potential actions the robot can perform with the object. In order to learn a robot-specific affordance predictor, we propose an interactive exploration pipeline which allows the robot to collect interaction experiences while exploring an unknown environment. We integrate an object-level map in the exploration pipeline such that the robot can identify different object instances and track objects across diverse viewpoints. This results in denser and more accurate affordance annotations compared to state-of-the-art methods, which do not incorporate a map. We show that our affordance exploration approach makes exploration more efficient and results in more accurate affordance prediction models compared to baseline methods.

Allocation for Omnidirectional Aerial Robots: Incorporating Power Dynamics

Tilt-rotor aerial robots are more dynamic and versatile than their fixed-rotor counterparts, since the thrust vector and body orientation are decoupled. However, the coordination of servomotors and propellers (the allocation problem) is not trivial, especially accounting for overactuation and actuator dynamics. We present and compare different methods of actuator allocation for tilt-rotor platforms, evaluating them on a real aerial robot performing dynamic trajectories. We extend the state-of-the-art geometric allocation into a differential allocation, which uses the platform's redundancy and does not suffer from singularities typical of the geometric solution. We expand it by incorporating actuator dynamics and introducing propeller limit curves. These improve the modeling of propeller limits, automatically balancing their usage and allowing the platform to selectively activate and deactivate propellers during flight. We show that actuator dynamics and limits make the tuning of the allocation not only easier, but also allow it to track more dynamic oscillating trajectories with angular velocities up to 4 rad/s, compared to 2.8 rad/s of geometric methods.

Evaluation of Human-Robot Interfaces based on 2D/3D Visual and Haptic Feedback for Aerial Manipulation

Most telemanipulation systems for aerial robots provide the operator with only 2D screen visual information. The lack of richer information about the robot's status and environment can limit human awareness and, in turn, task performance. While the pilot's experience can often compensate for this reduced flow of information, providing richer feedback is expected to reduce the cognitive workload and offer a more intuitive experience overall. This work aims to understand the significance of providing additional pieces of information during aerial telemanipulation, namely (i) 3D immersive visual feedback about the robot's surroundings through mixed reality (MR) and (ii) 3D haptic feedback about the robot interaction with the environment. To do so, we developed a human-robot interface able to provide this information. First, we demonstrate its potential in a real-world manipulation task requiring sub-centimeter-level accuracy. Then, we evaluate the individual effect of MR vision and haptic feedback on both dexterity and workload through a human subjects study involving a virtual block transportation task. Results show that both 3D MR vision and haptic feedback improve the operator's dexterity in the considered teleoperated aerial interaction tasks. Nevertheless, pilot experience remains the most significant factor.

Radar Meets Vision: Robustifying Monocular Metric Depth Prediction for Mobile Robotics

Mobile robots require accurate and robust depth measurements to understand and interact with the environment. While existing sensing modalities address this problem to some extent, recent research on monocular depth estimation has leveraged the information richness, yet low cost and simplicity of monocular cameras. These works have shown significant generalization capabilities, mainly in automotive and indoor settings. However, robots often operate in environments with limited scale cues, self-similar appearances, and low texture. In this work, we encode measurements from a low-cost mmWave radar into the input space of a state-of-the-art monocular depth estimation model. Despite the radar's extreme point cloud sparsity, our method demonstrates generalization and robustness across industrial and outdoor experiments. Our approach reduces the absolute relative error of depth predictions by 9-64% across a range of unseen, real-world validation datasets. Importantly, we maintain consistency of all performance metrics across all experiments and scene depths where current vision-only approaches fail. We further address the present deficit of training data in mobile robotics environments by introducing a novel methodology for synthesizing rendered, realistic learning datasets based on photogrammetric data that simulate the radar sensor observations for training. Our code, datasets, and pre-trained networks are made available at https://github.com/ethz-asl/radarmeetsvision.

Obstacle-Avoidant Leader Following with a Quadruped Robot

Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with daily tasks, or construction workers can have robots perform precision monitoring tasks on-site. However, manually steering a robot while in motion requires significant concentration from the operator, especially in tight or crowded spaces. This reduces walking speed, and the constant need for vigilance increases fatigue and, thus, the risk of accidents. This work presents a virtual leash with which a robot can naturally follow an operator. We use a sensor fusion based on a custom-built RF transponder, RGB cameras, and a LiDAR. In addition, we customize a local avoidance planner for legged platforms, which enables us to navigate dynamic and narrow environments. We successfully validate on the ANYmal platform the robustness and performance of our entire pipeline in real-world experiments.

A robust baro-radar-inertial odometry m-estimator for multicopter navigation in cities and forests

Search and rescue operations require mobile robots to navigate unstructured indoor and outdoor environments. In particular, actively stabilized multirotor drones need precise movement data to balance and avoid obstacles. Combining radial velocities from on-chip radar with MEMS inertial sensing has proven to provide robust, lightweight, and consistent state estimation, even in visually or geometrically degraded environments. Statistical tests robustify these estimators against radar outliers. However, available work with binary outlier filters lacks adaptability to various hardware setups and environments. Other work has predominantly been tested in handheld static environments or automotive contexts. This work introduces a robust baro-radar-inertial odometry (BRIO) m-estimator for quadcopter flights in typical GNSS-denied scenarios. Extensive real-world closed-loop flights in cities and forests demonstrate robustness to moving objects and ghost targets, maintaining a consistent performance with 0.5 % to 3.2 % drift per distance traveled. Benchmarks on public datasets validate the system's generalizability. The code, dataset, and video are available at https://github.com/ethz-asl/rio.