Pascal Gohl

The EuRoC micro aerial vehicle datasets

This paper presents visual-inertial datasets collected on-board a micro aerial vehicle. The datasets contain synchronized stereo images, IMU measurements and accurate ground truth. The first batch of datasets facilitates the design and evaluation of visual-inertial localization algorithms on real flight data. It was collected in an industrial environment and contains millimeter accurate position ground truth from a laser tracking system. The second batch of datasets is aimed at precise 3D environment reconstruction and was recorded in a room equipped with a motion capture system. The datasets contain 6D pose ground truth and a detailed 3D scan of the environment. Eleven datasets are provided in total, ranging from slow flights under good visual conditions to dynamic flights with motion blur and poor illumination, enabling researchers to thoroughly test and evaluate their algorithms. All datasets contain raw sensor measurements, spatio-temporally aligned sensor data and ground truth, extrinsic and intrinsic calibrations and datasets for custom calibrations.

Long-Endurance Sensing and Mapping Using a Hand-Launchable Solar-Powered UAV

This paper investigates and demonstrates the potential for very long endurance autonomous aerial sensing and mapping applications with AtlantikSolar, a small-sized, hand-launchable, solar-powered fixed-wing unmanned aerial vehicle. The platform design as well as the on-board state estimation, control and path-planning algorithms are overviewed. A versatile sensor payload integrating a multi-camera sensing system, extended on-board processing and high-bandwidth communication with the ground is developed. Extensive field experiments are provided including publicly demonstrated field-trials for search-and-rescue applications and long-term mapping applications. An endurance analysis shows that AtlantikSolar can provide full-daylight operation and a minimum flight endurance of 8 h throughout the whole year with its full multi-camera mapping payload. An open dataset with both raw and processed data is released and accompanies this paper contribution.

Dense visual-inertial navigation system for mobile robots

Real-time dense mapping and pose estimation is essential for a wide range of navigation tasks in mobile robotic applications. We propose an odometry and mapping system that leverages the full photometric information from a stereo-vision system as well as inertial measurements in a probabilistic framework while running in real-time on a single low-power Intel CPU core. Instead of performing mapping and localization on a set of sparse image features, we use the complete dense image intensity information in our navigation system. By incorporating a probabilistic model of the stereo sensor and the IMU, we can robustly estimate the ego-motion as well as a dense 3D model of the environment in real-time. The probabilistic formulation of the joint odometry estimation and mapping process enables to efficiently reject temporal outliers in ego-motion estimation as well as spatial outliers in the mapping process. To underline the versatility of the proposed navigation system, we evaluate it in a set of experiments on a multi-rotor system as well as on a quadrupedal walking robot. We tightly integrate our framework into the stabilization-loop of the UAV and the mapping framework of the walking robot. It is shown that the dense framework exhibits good tracking and mapping performance in terms of accuracy as well as robustness in scenarios with highly dynamic motion patterns while retaining a relatively small computational footprint. This makes it an ideal candidate for control and navigation tasks in unstructured GPS-denied environments, for a wide range of robotic platforms with power and weight constraints. The proposed framework is released as an open-source ROS package.

Visual industrial inspection using aerial robots

The use of unmanned aerial vehicles (UAV) offers a unique possibility to capture visual information in areas which are hard to reach or dangerous for humans. For UAVs to become a standard tool in visual inspection, it is of utmost importance that the aerial robot can be operated efficiently by a non-expert UAV pilot and that the navigation system is robust enough to remain operational in rough, industrial conditions. To this end, we present a UAV navigation system setup that uses visual-inertial sensor cues to estimate the UAV pose as well as to create a dense 3D map of the environment in real-time onboard the UAV, completely independent of GPS. The proposed navigation system enables the operator to directly interface the UAV using high-level commands such as waypoints or velocity commands while the navigation system ensures a stable and collision-free flight.

Shared control of autonomous vehicles based on velocity space optimization

This paper presents a method for shared control of a vehicle. The driver commands a preferred velocity which is transformed into a collision-free local motion that respects the actuator constraints and allows for smooth and safe control. Collision-free local motions are achieved with an extension of velocity obstacles that takes into account dynamic constraints and a grid-based map representation. To limit the freedom of the driver, a global guidance trajectory can be included, which specifies the areas where the vehicle is allowed to drive in each time instance. The low computational complexity of the method makes it well suited for multi-agent settings and high update rates and both a centralized and a distributed algorithm are provided that allow for real-time control of tens of vehicles. Extensive experimental results with real robotic wheelchairs at relatively high speeds in tight scenarios are presented.

Omnidirectional visual obstacle detection using embedded FPGA

For autonomous navigation of Micro Aerial Vehicles (MAVs) in cluttered environments, it is essential to detect potential obstacles not only in the direction of flight but in their entire local environment. While there exist systems that do vision based obstacle detection, most of them are limited to a single perception direction. Extending these systems to a multi-directional sensing approach would exhaust the payload limit in terms of weight and computational power. We present a novel light-weight sensor setup comprising of four stereo heads and an inertial measurement unit (IMU) to perform FPGA-based dense reconstruction for obstacle detection in all directions. As the data-rate scales up with the number of cameras we use an FPGA to perform streaming based tasks in real-time and show a light-weight polar-coordinate map to allow a companion computer to fully process the data of all the cameras and perform obstacle detection in real-time. The system is able to process up to 80 frames per second (fps) freely distributed on the four stereo heads while maintaining a low power budget. The perception system including FPGA, image sensors and stereo mounts is 235 g in weight.

Towards autonomous mine inspection

The purpose of this paper is to evaluate the use of a micro aerial vehicle (MAV) for autonomous inspection and 3D reconstruction of underground mines. The goal is to manually fly an MAV equipped with cameras and a laser range sensor into a vertical shaft to collect data. This data can be used to evaluate the performance of the localization system as well as post processed to reconstruct a 3D model of the shaft. Due to its novelty of flying an MAV in a deep mine, we report gained experience of the effect of the hot, wet and dusty environment on the system as well as the influence of turbulences from vertical winds on the flight performance. Further we evaluated the quality of the recorded data and there applicability for a fully autonomous mine inspection system.

The CoaX Micro-helicopter: A Flying Platform for Education and Research

CoaX is a micro-helicopter designed for the research and education markets by Skybotix AG in Switzerland. It is a unique robotic coaxial helicopter equipped with state of the art sensors and processors: an integrated Inertial Measurement Unit (IMU), a pressure sensor, a down-looking sonar, three side looking range sensors and a color camera. To communicate with a ground station, the robot has a Bluetooth (or XBee) module and an optional WiFi module. Additionally, the CoaX supports the Overo series of tiny computers from Gumstix and is ready to fly out of the box with a set of attitude and altitude control functions. One can also control the system through an open-source API to give high-level commands for taking-off, landing or any other type of motion. In addition to presenting the CoaX, this paper reports on three experiments conducted to demonstrate the system’s motto: “simple to fly, simple to program, simple to extend”.