Pascal Gohl

Bio: Pascal Gohl is an academic researcher from Institute of Robotics and Intelligent Systems. The author has contributed to research in topics: Navigation system & Inertial measurement unit. The author has an hindex of 9, co-authored 17 publications receiving 1327 citations. Previous affiliations of Pascal Gohl include ETH Zurich.

The EuRoC micro aerial vehicle datasets

Abstract: 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.

A synchronized visual-inertial sensor system with FPGA pre-processing for accurate real-time SLAM

Abstract: Robust, accurate pose estimation and mapping at real-time in six dimensions is a primary need of mobile robots, in particular flying Micro Aerial Vehicles (MAVs), which still perform their impressive maneuvers mostly in controlled environments. This work presents a visual-inertial sensor unit aimed at effortless deployment on robots in order to equip them with robust real-time Simultaneous Localization and Mapping (SLAM) capabilities, and to facilitate research on this important topic at a low entry barrier. Up to four cameras are interfaced through a modern ARMFPGA system, along with an Inertial Measurement Unit (IMU) providing high-quality rate gyro and accelerometer measurements, calibrated and hardware-synchronized with the images. This facilitates a tight fusion of visual and inertial cues that leads to a level of robustness and accuracy which is difficult to achieve with purely visual SLAM systems. In addition to raw data, the sensor head provides FPGA-pre-processed data such as visual keypoints, reducing the computational complexity of SLAM algorithms significantly and enabling employment on resource-constrained platforms. Sensor selection, hardware and firmware design, as well as intrinsic and extrinsic calibration are addressed in this work. Results from a tightly coupled reference visual-inertial SLAM framework demonstrate the capabilities of the presented system.

Visual industrial inspection using aerial robots

Abstract: 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.

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

Abstract: 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

Abstract: 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.

ORB-SLAM2: An Open-Source SLAM System for Monocular, Stereo, and RGB-D Cameras

Abstract: We present ORB-SLAM2, a complete simultaneous localization and mapping (SLAM) system for monocular, stereo and RGB-D cameras, including map reuse, loop closing, and relocalization capabilities. The system works in real time on standard central processing units in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end, based on bundle adjustment with monocular and stereo observations, allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches with map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

ORB-SLAM2: an Open-Source SLAM System for Monocular, Stereo and RGB-D Cameras

Abstract: We present ORB-SLAM2 a complete SLAM system for monocular, stereo and RGB-D cameras, including map reuse, loop closing and relocalization capabilities. The system works in real-time on standard CPUs in a wide variety of environments from small hand-held indoors sequences, to drones flying in industrial environments and cars driving around a city. Our back-end based on bundle adjustment with monocular and stereo observations allows for accurate trajectory estimation with metric scale. Our system includes a lightweight localization mode that leverages visual odometry tracks for unmapped regions and matches to map points that allow for zero-drift localization. The evaluation on 29 popular public sequences shows that our method achieves state-of-the-art accuracy, being in most cases the most accurate SLAM solution. We publish the source code, not only for the benefit of the SLAM community, but with the aim of being an out-of-the-box SLAM solution for researchers in other fields.

VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator

Abstract: One camera and one low-cost inertial measurement unit (IMU) form a monocular visual-inertial system (VINS), which is the minimum sensor suite (in size, weight, and power) for the metric six degrees-of-freedom (DOF) state estimation. In this paper, we present VINS-Mono: a robust and versatile monocular visual-inertial state estimator. Our approach starts with a robust procedure for estimator initialization. A tightly coupled, nonlinear optimization-based method is used to obtain highly accurate visual-inertial odometry by fusing preintegrated IMU measurements and feature observations. A loop detection module, in combination with our tightly coupled formulation, enables relocalization with minimum computation. We additionally perform 4-DOF pose graph optimization to enforce the global consistency. Furthermore, the proposed system can reuse a map by saving and loading it in an efficient way. The current and previous maps can be merged together by the global pose graph optimization. We validate the performance of our system on public datasets and real-world experiments and compare against other state-of-the-art algorithms. We also perform an onboard closed-loop autonomous flight on the microaerial-vehicle platform and port the algorithm to an iOS-based demonstration. We highlight that the proposed work is a reliable, complete, and versatile system that is applicable for different applications that require high accuracy in localization. We open source our implementations for both PCs ( https://github.com/HKUST-Aerial-Robotics/VINS-Mono ) and iOS mobile devices ( https://github.com/HKUST-Aerial-Robotics/VINS-Mobile ).

Direct Sparse Odometry

Abstract: Direct Sparse Odometry (DSO) is a visual odometry method based on a novel, highly accurate sparse and direct structure and motion formulation. It combines a fully direct probabilistic model (minimizing a photometric error) with consistent, joint optimization of all model parameters, including geometry-represented as inverse depth in a reference frame-and camera motion. This is achieved in real time by omitting the smoothness prior used in other direct methods and instead sampling pixels evenly throughout the images. Since our method does not depend on keypoint detectors or descriptors, it can naturally sample pixels from across all image regions that have intensity gradient, including edges or smooth intensity variations on essentially featureless walls. The proposed model integrates a full photometric calibration, accounting for exposure time, lens vignetting, and non-linear response functions. We thoroughly evaluate our method on three different datasets comprising several hours of video. The experiments show that the presented approach significantly outperforms state-of-the-art direct and indirect methods in a variety of real-world settings, both in terms of tracking accuracy and robustness.

Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Age

Abstract: Simultaneous Localization and Mapping (SLAM)consists in the concurrent construction of a model of the environment (the map), and the estimation of the state of the robot moving within it. The SLAM community has made astonishing progress over the last 30 years, enabling large-scale real-world applications, and witnessing a steady transition of this technology to industry. We survey the current state of SLAM. We start by presenting what is now the de-facto standard formulation for SLAM. We then review related work, covering a broad set of topics including robustness and scalability in long-term mapping, metric and semantic representations for mapping, theoretical performance guarantees, active SLAM and exploration, and other new frontiers. This paper simultaneously serves as a position paper and tutorial to those who are users of SLAM. By looking at the published research with a critical eye, we delineate open challenges and new research issues, that still deserve careful scientific investigation. The paper also contains the authors' take on two questions that often animate discussions during robotics conferences: Do robots need SLAM? and Is SLAM solved?