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Projection of LADAR data to base ground planes is shown in (a). The extracted features (corners) from the UGV (black) and UAV (white) LADARs are shown as white and black squares, respectively, in (b).
Source publication
An iterative temporal registration algorithm is presented in this article for registering 3D range images obtained from unmanned
ground and aerial vehicles traversing unstructured environments. We are primarily motivated by the development of 3D registration
algorithms to overcome both the unavailability and unreliability of Global Positioning Syst...
Contexts in source publication
Context 1
... the UGV, the ground values are obtained from the LADAR points that are within a given radius immediately in front of the vehicle and those for the UAV are obtained by finding the minimum of the LADAR values. Then we project the UAV and UGV LADAR data into the base ground planes as depicted in Figure 2(a) and construct the feature planes by using the Canny edge detector [18]. The corner features are detected based on the intersections of lines formed by edges. ...
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Citations
... Para la navegación autónoma es fundamental que el UGV disponga de sensores a bordo que faciliten información actualizada del entorno (sensores exteroceptivos) y del comportamiento del propio robot (sensores propioceptivos) [17]. Los tipos de sensores empleados más comúnmente son: unidades de medida inercial o Inertial Measurement Unit (IMU) [18], cámaras RGB [19], cámaras estéreo [20], cámaras de tiempo de vuelo o Time of Flight (TOF) [21], telémetros o LIDAR bidimensionales [22] o tridimensionales [23] y GPS [24] entre otros. Resulta habitual utilizar distintos tipos de sensores de forma combinada para obtener información fiable sobre la que planificar los movimientos del robot [25]. ...
This thesis addresses possible solutions for reactive navigation of the ground mobile robot Andabata, whose main exteroceptive sensor is a three-dimensional (3D) laser scanner, at low-speed in natural environments, where numerous challenges arise due to the complexity of the terrain.
To this end, the Robot Operating System (ROS) is used to organize the robot's software and to process the information from the on-board sensors in order to carry out various navigation strategies.
The first strategy consists on building fuzzy elevation maps that describe the environment close to the vehicle. This strategy is quite novel but implies a high computational load that reduces robot's reactivity.
To alleviate this load, training ground traversability classifiers using supervised learning is proposed. For this purpose, it is necessary to have 3D point clouds labeled without errors that have been synthetically generated with the robotic simulator Gazebo.
Finally, a new local navigation strategy based on the classification of the points obtained by the 3D rangefinder is analyzed. To do so, two-dimensional (2D) traversability maps are built that allow Andábata to avoid in a reactive way both negative and positive obstacles.
... Similarly, the authors of [8,9] examined the registration of LIDAR between aerial and ground vehicles by adapting the iterative closest point (ICP). ...
This work presents a collaborative unmanned aerial and ground vehicle system which utilizes the aerial vehicle’s overhead view to inform the ground vehicle’s path planning in real time. The aerial vehicle acquires imagery which is assembled into a orthomosaic and then classified. These terrain classes are used to estimate relative navigation costs for the ground vehicle so energy-efficient paths may be generated and then executed. The two vehicles are registered in a common coordinate frame using a real-time kinematic global positioning system (RTK GPS) and all image processing is performed onboard the unmanned aerial vehicle, which minimizes the data exchanged between the vehicles. This paper describes the architecture of the system and quantifies the registration errors between the vehicles.
... Research on formation control also focused on better swarming of autonomous robot groups. References [11], [12], [13] and [14] includes notable studies based on formation control of unmanned vehicle groups. ...
In the past decade Unmanned Vehicles have become a topic of interest in many research organizations. Unmanned vehicles based systems are integrated to many applications in various areas ranging from military missions to wildfire detection. Recently, there has been a great interest to design collaborative systems of unmanned aerial vehicles and unmanned ground vehicles. This paper proposes a new algorithm that can be used to calculate optimal paths of unmanned air vehicles and quantities of unmanned ground and air vehicles to cover a target area. This algorithm is useful for patrolling systems based on the collaboration of unmanned air vehicles and unmanned ground vehicles.
... There are noteworthy studies related with object tracking, path planning and localization. The significant examples of these studies including uavs and ugvs can be found in [9], [10] and [11]. Formation control of a group of autonomous vehicles/robots is one of the important applications of cooperative control. ...
... The processed data is used as an input for a UGV planner to navigate in the covered area. Other related work using AGC for cooperative navigation and state estimation can be found in [14], [15], [16], [17], [18], [11], [19], [20], [21]. ...
We present in this paper a decentralized multi-robot (aerial and ground) cooperation scheme for objects trans-portation. A team of ground mobile robots guided by a drone and a human operator moves in a coordinated way keeping a predefined formation in order to carry objects (tools, gas masks,...) in unsafe industrial areas. One ground mobile robot (leader) navigates among obstacles thanks to the waypoints provided by the drone and the human operator. The other ground mobile robots (followers) use a predictive vision based target tracking controller to keep a certain distance and bearing to the leader.
... The processed data is used as an input for a UGV planner to navigate in the covered area. Other related work using AGC for cooperative navigation and state estimation can be found in [14], [15], [16], [17], [18], [11], [19], [20], [21]. ...
We present in this paper a decentralized multi-robot (aerial and ground) cooperation scheme for objects trans-portation. A team of ground mobile robots guided by a drone and a human operator moves in a coordinated way keeping a predefined formation in order to carry objects (tools, gas masks,...) in unsafe industrial areas. One ground mobile robot (leader) navigates among obstacles thanks to the waypoints provided by the drone and the human operator. The other ground mobile robots (followers) use a predictive vision based target tracking controller to keep a certain distance and bearing to the leader.
... al. [14] , Madhavan et. al. [15], Zengin et. al. [16], Choi et. ...
In the past decade Unmanned Vehicles have become a topic of interest in many research organizations. Unmanned Vehicles based systems are finding applications in various areas ranging from military applications to wildfire detection. Recently, there has been a great interest to design collaborative systems of unmanned air vehicles(UAV) and unmanned ground vehicles(UGV). This paper is a survey of UAV/UGV collaborative systems. A summary of the various UAV/UGV collaboration frameworks and approaches has been given. Finally, the paper investigates the potential applications of related systems.
... Fig. 4.16 shows fitted curves, using the piecewise least squares curve fitting. (Besl and McKay, 1992;Madhavan et al., 2005) For the sake of simplicity, the ICP technique will be discussed for the 2D case, as the generalization to 3D is straightforward. Note that in our application this is generally the case, as the road surface is almost flat, although not necessarily horizontal but for smaller areas can be almost always modeled by a plane. ...
The widespread use of digital technologies, combined with rapid sensor advancements resulted in a paradigm shift in geospatial technologies the end of the last millennium. The improved performance provided by the state-of-the-art airborne remote sensing technology created opportunities for new applications that require high spatial and temporal resolution data. Transportation activities represent a major segment of the economy in industrialized nations. As such both the transportation infrastructure and traffic must be carefully monitored and planned. Engineering scale topographic mapping has been a long-time geospatial data user, but the high resolution geospatial data could also be considered for vehicle extraction and velocity estimation to support traffic flow analysis. The objective of this dissertation is to provide an assessment on what state-of-the-art remote sensing technologies can offer in both areas: first, to further improve the accuracy and reliability of topographic, in particular, roadway corridor mapping systems, and second, to assess the feasibility of extracting primary data to support traffic flow computation. The discussion is concerned with airborne LiDAR (Light Detection And Ranging) and digital camera systems, supported by direct georeferencing. The review of the state-of-the-art remote sensing technologies is dedicated to address the special requirements of the two transportation applications of airborne remotely sensed data. The performance characteristics of the geospatial sensors and the overall error budget are discussed. The error analysis part is focused on the overall achievable point positioning accuracy performance of directly georeferenced remote sensing systems. The QA/QC (Quality Assurance/Quality Control) process is a challenge for any airborne direct georeferencing-based remote sensing system. A new method to support QA/QC is introduced that uses the road pavement markings to improve both sensor data accuracy as well as the position of road features. The identification of the pavement markings is based on LiDAR intensity data and is guided by the ground control information available. The centerline of the markings in LiDAR data is modeled and matched to the reference data, providing the observation to the QA/QC process. The discussion on the innovative use of remote sensing technologies investigates the feasibility of providing remotely sensed traffic data for monitoring and management. An advantage of air-based platforms, including manned and unmanned fixed-wing aircraft and helicopters, is that they can be rapidly deployed to observe traffic incidents that occur in areas where there are no ground-based sensors. To support vehicle extraction from airborne imagery, a method was introduced that provides a true object scale data representation that can facilitate the vehicle extraction. The vehicle extraction from LiDAR data was followed by coarse classification of the extracted vehicles to support coarse velocity estimation; basically, grouping the vehicles into three major categories based on their size. Finally, a novel method was introduced for simultaneously acquired LiDAR and image data, which can combine the advantages of the two sensors for obtaining better velocity estimates of LiDAR-extracted vehicles.
... Assuming that the two representations, such as the curve fitted ones, provide an adequate description of the same shape, the free-shape curve matching techniques can be applied. Since the pavement markings' descriptions in both original and curve-fitted representations for both LiDAR and GPS-surveyed points are spatially close to each other, the wellknown Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992;Madhavan et al., 2005) was selected to perform this task. ...
LiDAR technology, the primary source of highly accurate surface data at large scale, has seen remarkable developments in recent years. Specifically, the accuracy of the laser ranging has reached the few cm level for hard surfaces, close to static survey performance, and the point density has increased significantly, as a result of higher pulse rates, such as 150 kHz PRF for multipulse LiDAR systems. The high ranging accuracy of the laser sensor also means that the overall accuracy of the point cloud is now predominantly determined by the quality of the navigation solution (typically based on GPS/IMU sensor integration), which is also advancing. All these developments allow for better surface representation in terms of denser point cloud with highly accurate point coordinates. Furthermore, because of the increased point density, the horizontal accuracy has become an equally important part of the product characterization. In parallel to these developments, the demand for better QA/QC is also growing, and now the characterization of the LiDAR products includes the horizontal accuracy. Except for relative measures, there is no reliable way to assess the positioning quality of the data captured by any imaging sensor system, which is based on direct georeferencing, and therefore, using some ground control is almost mandatory if high accuracy is required. This paper introduces a method to use road pavement marking as ground control that could be used for QA/QC. These linear features are widely available in urban areas and along transportation corridors, where most of the government and commercial mapping takes place, and an additional advantage of using pavement markings is that they can be quickly surveyed with various GPS echnique (RTK, VRS, post-processed).
... Iterative registration algorithms are increasingly used for registering 2D/3D curves and range images recently. The wellknown Iterative Closest Point (ICP) algorithm (Besl and McKay, 1992;Madhavan et al., 2005) is adopted here to match curves describing pavement markings obtained from LiDAR intensity and GPS measurements. The ICP algorithm finds the best correspondence between two curves (point sets) by iteratively determining the translations and rotations parameters of a 2D/3D rigid body transformation. ...
LiDAR technology became an indispensable airborne mapping tool in recent years and is the primary source of highly accurate surface data at large scale. Although, the ranging accuracy of the laser sensor strongly depends on the surface characteristics, by and large, it falls in to the few cm range. This also implies that the achieved accuracy of a LiDAR system, defined in terms of the absolute accuracy of the laser points, is predominantly determined by the quality of the navigation solution (typically based on GPS/IMU sensor integration). Despite significant advancements in navigation technologies recently, to achieve and sustain a high accuracy navigation solution of an airborne platform for extended time is still a difficult task. Furthermore, there is no reliable way to assess the positioning quality of the data captured by any imaging sensor systems, which are based on direct georeferencing. Therefore, using some ground control is almost mandatory if high accuracy is required. This paper introduces a method to use road pavement marking as ground control that could be used for QA/QC. These linear features are widely available in urban areas and along transportation corridors, where most of the government and commercial mapping takes place. A key advantage of using pavement markings is that they can be quickly surveyed with GPS VRS technique.
