Ground Profile Recovery from Aerial 3D LiDAR-based Maps

The paper presents the study and implementation of the ground detection methodology with filtration and removal of forest points from LiDAR-based 3D point cloud using the Cloth Simulation Filtering (CSF) algorithm. The methodology allows to recover a terrestrial relief and create a landscape map of a forestry region. As the proof-of-concept, we provided the outdoor flight experiment, launching a hexacopter under a mixed forestry region with sharp ground changes nearby Innopolis city (Russia), which demonstrated the encouraging results for both ground detection and methodology robustness.Comment: 8 pages, FRUCT-2019 conferenc

Pushbroom Stereo for High-Speed Navigation in Cluttered Environments

We present a novel stereo vision algorithm that is capable of obstacle detection on a mobile-CPU processor at 120 frames per second. Our system performs a subset of standard block-matching stereo processing, searching only for obstacles at a single depth. By using an onboard IMU and state-estimator, we can recover the position of obstacles at all other depths, building and updating a full depth-map at framerate. Here, we describe both the algorithm and our implementation on a high-speed, small UAV, flying at over 20 MPH (9 m/s) close to obstacles. The system requires no external sensing or computation and is, to the best of our knowledge, the first high-framerate stereo detection system running onboard a small UAV

Characterization of a mobile mapping system for seamless navigation

4noMobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone.openopenDI Pietra V.; Grasso N.; Piras M.; Dabove P.DI Pietra, V.; Grasso, N.; Piras, M.; Dabove, P

Tightly Coupled 3D Lidar Inertial Odometry and Mapping

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidar-IMU odometry (LIO) can perform well with acceptable drift after long-term experiment, even in challenging cases where the lidar measurements can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.Comment: Accepted by ICRA 201

CHARACTERIZATION OF A MOBILE MAPPING SYSTEM FOR SEAMLESS NAVIGATION

Abstract. Mobile Mapping Systems (MMS) are multi-sensor technologies based on SLAM procedure, which provides accurate 3D measurement and mapping of the environment as also trajectory estimation for autonomous navigation. The major limits of these algorithms are the navigation and mapping inconsistence over the time and the georeferencing of the products. These issues are particularly relevant for pose estimation regardless the environment like in seamless navigation. This paper is a preliminary analysis on a proposed multi-sensor platform integrated for indoor/outdoor seamless positioning system. In particular the work is devoted to analyze the performances of the MMS in term of positioning accuracy and to evaluate its improvement with the integration of GNSS and UWB technology. The results show that, if the GNSS and UWB signal are not degraded, using the correct weight to their observations in the Stencil estimation algorithm, is possible to obtain an improvement in the accuracy of the MMS navigation solution as also in the global consistency of the final point cloud. This improvement is measured in about 7 cm for planimetric coordinate and 34 cm along the elevation with respect to the use of the Stencil system alone

Toward a unified PNT, Part 1: Complexity and context: Key challenges of multisensor positioning

The next generation of navigation and positioning systems must provide greater accuracy and reliability in a range of challenging environments to meet the needs of a variety of mission-critical applications. No single navigation technology is robust enough to meet these requirements on its own, so a multisensor solution is required. Known environmental features, such as signs, buildings, terrain height variation, and magnetic anomalies, may or may not be available for positioning. The system could be stationary, carried by a pedestrian, or on any type of land, sea, or air vehicle. Furthermore, for many applications, the environment and host behavior are subject to change. A multi-sensor solution is thus required. The expert knowledge problem is compounded by the fact that different modules in an integrated navigation system are often supplied by different organizations, who may be reluctant to share necessary design information if this is considered to be intellectual property that must be protected

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic