Marius Beul

Multilayered Mapping and Navigation for Autonomous Micro Aerial Vehicles

Micro aerial vehicles, such as multirotors, are particularly well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance or disaster management. Key prerequisites for the fully autonomous operation of micro aerial vehicles are real-time obstacle detection and planning of collision-free trajectories. In this article, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a three-dimensional 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. Local maps are efficiently merged in order to simultaneously build global maps of the environment and localize in these. For autonomous navigation, we generate trajectories in a multilayered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach and the involved components in simulation and with the real autonomous micro aerial vehicle. Finally, we present the results of a complete mission for autonomously mapping a building and its surroundings.

Obstacle detection and navigation planning for autonomous micro aerial vehicles

Obstacle detection and real-time planning of collision-free trajectories are key for the fully autonomous operation of micro aerial vehicles in restricted environments. In this paper, we propose a complete system with a multimodal sensor setup for omnidirectional obstacle perception consisting of a 3D laser scanner, two stereo camera pairs, and ultrasonic distance sensors. Detected obstacles are aggregated in egocentric local multiresolution grid maps. We generate trajectories in a multi-layered approach: from mission planning to global and local trajectory planning, to reactive obstacle avoidance. We evaluate our approach in simulation and with the real autonomous micro aerial vehicle.

Autonomous Navigation for Micro Aerial Vehicles in Complex GNSS-denied Environments

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this article, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

A high-performance MAV for autonomous navigation in complex 3D environments

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous monitoring, inspection, and surveillance of buildings, e.g., for maintenance in industrial plants. Key prerequisites for the fully autonomous operation of micro aerial vehicles in complex 3D environments include real-time state estimation, obstacle detection, mapping, and navigation planning. In this paper, we describe an integrated system with a multimodal sensor setup for omnidirectional environment perception and 6D state estimation. Our MAV is equipped with a variety of sensors including a dual 3D laser scanner, three stereo camera pairs, an IMU and a powerful onboard computer to achieve these tasks in real-time. Our experimental evaluation demonstrates the performance of the integrated system.

NimbRo Rescue: Solving Disaster-response Tasks with the Mobile Manipulation Robot Momaro

Robots that solve complex tasks in environments too dangerous for humans to enter are desperately needed, e.g., for search and rescue applications. We describe our mobile manipulation robot Momaro, with which we participated successfully in the DARPA Robotics Challenge. It features a unique locomotion design with four legs ending in steerable wheels, which allows it both to drive omnidirectionally and to step over obstacles or climb. Furthermore, we present advanced communication and teleoperation approaches, which include immersive three-dimensional 3D visualization, and 6D tracking of operator head and arm motions. The proposed system is evaluated in the DARPA Robotics Challenge, the DLR SpaceBot Cup Qualification, and lab experiments. We also discuss the lessons learned from the competitions.

Collaborative object picking and delivery with a team of micro aerial vehicles at MBZIRC

Picking and transporting objects in an outdoor environment with multiple lightweight MAVs is a demanding task. The main challenges are sudden changes of flight dynamics due to altered center of mass and weight, varying lighting conditions for visual perception, and coordination of the MAVs over unreliable wireless connections. At the Mohamed Bin Zayed International Robotics Challenge (MBZIRC) teams competed in a Treasure Hunt where three MAVs had to collaboratively pick colored disks and drop them into a designated box. Only little preparation and test time on- site required robust algorithms and easily maintainable systems to successfully achieve the challenge objectives. We describe our multi-robot system employed at MBZIRC, including a lightweight gripper, a vision system robust against illumination and color changes, and a control architecture allowing to operate multiple robots safely. With our system, we—as part of the larger team NimbRo of ground and flying robots—won the Grand Challenge and achieved a third place in the Treasure Hunt.

Analytical time-optimal trajectory generation and control for multirotors

Micro aerial vehicles, such as multirotors, are particularly well suited for autonomous monitoring, inspection, and surveillance, e.g., for doing inventory in large warehouses. Key prerequisites for efficient MAV inventory missions is time- and energy-efficient flight. Generated trajectories must assure the visiting of certain waypoints while preventing large deviations or overshoot, as these can cause crashes in narrow corridors. In this paper, we propose a novel trajectory generation method that is able to analytically compute time- and energy-optimal trajectories, incorporating system dynamics, based on a first-principles model. Due to the fast runtime of the method, it is not only used to generate trajectories on a higher level, but to control the MAV in real-time, substituting state-of-the-art cascaded control loops for position, velocity, and attitude.

Supervised Autonomy for Exploration and Mobile Manipulation in Rough Terrain with a Centaur-Like Robot

Planetary exploration scenarios illustrate the need for autonomous robots that are capable to operate in unknown environments without direct human interaction. At the DARPA Robotics Challenge, we demonstrated that our Centaur-like mobile manipulation robot Momaro can solve complex tasks when teleoperated. Motivated by the DLR SpaceBot Cup 2015, where robots should explore a Mars-like environment, find and transport objects, take a soil sample, and perform assembly tasks, we developed autonomous capabilities for Momaro. Our robot perceives and maps previously unknown, uneven terrain using a 3D laser scanner. Based on the generated height map, we assess drivability, plan navigation paths, and execute them using the omnidirectional drive. Using its four legs, the robot adapts to the slope of the terrain. Momaro perceives objects with cameras, estimates their pose, and manipulates them with its two arms autonomously. For specifying missions, monitoring mission progress, on-the-fly reconfiguration, and teleoperation, we developed a ground station with suitable operator interfaces. To handle network communication interruptions and latencies between robot and ground station, we implemented a robust network layer for the ROS middleware. With the developed system, our team NimbRo Explorer solved all tasks of the DLR SpaceBot Camp 2015. We also discuss the lessons learned from this demonstration.

Autonomous MAV navigation in complex GNSS-denied 3D environments

Micro aerial vehicles, such as multirotors, are particular well suited for the autonomous exploration, examination, and surveillance of otherwise inaccessible areas, e.g., for search and rescue missions in indoor disaster sites. Key prerequisites for the fully autonomous operation of micro aerial vehicles in restricted environments are 3D mapping, real-time pose tracking, obstacle detection, and planning of collision-free trajectories. In this work, we propose a complete navigation system with a multimodal sensor setup for omnidirectional environment perception. Measurements of a 3D laser scanner are aggregated in egocentric local multiresolution grid maps. Local maps are registered and merged to allocentric maps in which the MAV localizes. For autonomous navigation, we generate trajectories in a multi-layered approach: from mission planning over global and local trajectory planning to reactive obstacle avoidance. We evaluate our approach in a GNSS-denied indoor environment where multiple collision hazards require reliable omnidirectional perception and quick navigation reactions.

Team NimbRo Rescue at DARPA Robotics Challenge Finals

Summary form only given. The video shows the compacted first-day run of team NimbRo Rescue at the DARPA Robotics Challenge Finals in Pomona, CA. It features the mobile manipulation robot Momaro which has a flexible base with four legs that end in steerable wheels. Momaro can drive omnidirectionally and step over obstacles. The robot is equipped with an anthropomorphic upper body with two 7 DoF arms that end in four-finger grippers. A 3D laser scanner and multiple cameras capture the environment. Operator interfaces include a steering wheel and a gas pedal for car driving, a joystick for omnidirectional locomotion, and a head-mounted 3D immersive display with two 6 DoF magnetic hand trackers for solving complex manipulation tasks. Through Momaro, our team solved seven of the eight tasks of the DARPA Robotics Challenge: driving a car, egressing the car, opening a door, turning a valve, cutting a hole into a drywall, traversing debris, and a surprise task, which was to operate a big switch. All this was done in only 34 minutes. Team NimbRo Rescue was the best European team, coming in 4th in the overall ranking.