Quantitative analysis of non-linear probabilistic state estimation filters for deployment on dynamic unmanned systems

This paper presents an offline ground truth generation method using LIDAR(Light Detection and Ranging) scans and odometry. The generated ground truth allows quantitative evaluation of the performance of self-localization methods in urban areas where GNSS(Global Navigation Satellite System) cannot be trusted. The proposed method determines the vehicle pose (position and orientation) by aligning the LIDAR input with previously collected point cloud data. However, as alignment convergences are affected by the environment around the vehicle during each LIDAR scan, it can be erroneous. Incorrect estimates are removed and poses are interpolated by relying on odometry; which is locally accurate. A step by step optimization approach is adopted to yield the most accurate result. Experiments performed in a typical urban environment, with many buildings and surrounding obstacles, demonstrated the effectiveness of the proposed method.

3D LIDARs (Light Detection and Ranging) with 1.5μm nanosecond pulse lasers have been increasingly used in different applications. The main reason for their popularity is that these LIDARs have high performance while at the same time can be made eye-safe. Because the laser hazard effect on eyes or skin at this wavelength region (<1.4μm) is mainly from the thermal effect accumulated from many individual pulses over a period of seconds, scanning can effectively reduce the laser beam hazard effect from the LIDARs. Neptec LIDARs have been used in docking to the International Space Station, military helicopter landing and industrial mining applications. We have incorporated the laser safety requirements in the LIDAR design and conducted laser safety analysis for different operational scenarios. While 1.5μm is normally said to be the eye-safe wavelength, in reality a high performance 3D LIDAR needs high pulse energy, small beam size and high pulse repetition frequency (PRF) to achieve long range, high resolution and high density images. The resulting radiant exposure of its stationary beam could be many times higher than the limit for a Class 1 laser device. Without carefully choosing laser and scanning parameters, including field-of-view, scan speed and pattern, a scanning LIDAR can’t be eye- or skin-safe based only on its wavelength. This paper discusses the laser safety considerations in the design of eye-safe scanning LIDARs, including laser pulse energy, PRF, beam size and scanning parameters in two basic designs of scanning mechanisms, i.e. galvanometer based scanner and Risley prism based scanner. The laser safety is discussed in terms of device classification, nominal ocular hazard distance (NOHD) and safety glasses optical density (OD).

Routine bridge inspections require labor intensive and highly subjective visual interpretation to determine bridge deck surface condition. Light Detection and Ranging (LiDAR) a relatively new class of survey instrument has become a popular and increasingly used technology for providing as-built and inventory data in civil applications. While an increasing number of private and governmental agencies possess terrestrial and mobile LiDAR systems, an understanding of the technology’s capabilities and potential applications continues to evolve. LiDAR is a line-of-sight instrument and as such, care must be taken when establishing scan locations and resolution to allow the capture of data at an adequate resolution for defining features that contribute to the analysis of bridge deck surface condition. Information such as the location, area, and volume of spalling on deck surfaces, undersides, and support columns can be derived from properly collected LiDAR point clouds. The LiDAR point clouds contain information that can provide quantitative surface condition information, resulting in more accurate structural health monitoring. LiDAR scans were collected at three study bridges, each of which displayed a varying degree of degradation. A variety of commercially available analysis tools and an independently developed algorithm written in ArcGIS Python (ArcPy) were used to locate and quantify surface defects such as location, volume, and area of spalls. The results were visual and numerically displayed in a user-friendly web-based decision support tool integrating prior bridge condition metrics for comparison. LiDAR data processing procedures along with strengths and limitations of point clouds for defining features useful for assessing bridge deck condition are discussed. Point cloud density and incidence angle are two attributes that must be managed carefully to ensure data collected are of high quality and useful for bridge condition evaluation. When collected properly to ensure effective evaluation of bridge surface condition, LiDAR data can be analyzed to provide a useful data set from which to derive bridge deck condition information.

Simultaneous localization and mapping (SLAM) has been investigated in the field of robotics for two decades, as it is considered to be an effective method for solving the positioning and mapping problem in a single framework. In the SLAM community, the Extended Kalman Filter (EKF) based SLAM and particle filter SLAM are the most mature technologies. After years of development, graph-based SLAM is becoming the most promising technology and a lot of progress has been made recently with respect to accuracy and efficiency. No matter which SLAM method is used, loop closure is a vital part for overcoming the accumulated errors. However, in 2D Light Detection and Ranging (LiDAR) SLAM, on one hand, it is relatively difficult to extract distinctive features in LiDAR scans for loop closure detection, as 2D LiDAR scans encode much less information than images; on the other hand, there is also some special mapping scenery, where no loop closure exists. Thereby, in this paper, instead of loop closure detection, we first propose the method to introduce extra control network constraint (CNC) to the back-end optimization of graph-based SLAM, by aligning the LiDAR scan center with the control vertex of the presurveyed control network to optimize all the poses of scans and submaps. Field tests were carried out in a typical urban Global Navigation Satellite System (GNSS) weak outdoor area. The results prove that the position Root Mean Square (RMS) error of the selected key points is 0.3614 m, evaluated with a reference map produced by Terrestrial Laser Scanner (TLS). Mapping accuracy is significantly improved, compared to the mapping RMS of 1.6462 m without control network constraint. Adding distance constraints of the control network to the back-end optimization is an effective and practical method to solve the drift accumulation of LiDAR front-end scan matching.

The demand for a reliable and accurate navigation system that can replace Global Positioning System (GPS) in GPS-denied environment has become increasingly imperative. For indoor environment where GPS is almost unavailable or unreliable, the utilization of other sensors such as inertial sensors becomes necessary. However, inertial sensors alone cannot sustain reliable long-term accuracy due to errors accumulation without external periodic corrections. Thus this paper proposes the utilization of Light Detection and Ranging (LiDAR) as an alternative system to provide periodic corrections. In this paper, a tightly-coupled integrated navigation system that integrates LiDAR, a single-axis gyroscope and wheel encoder is introduced. Straight lines detection and extraction algorithm is utilized to estimate the changes in orientation and range from LiDAR to the extracted line. LiDAR-estimated orientation change and range change to the extracted line feature between two consecutive LiDAR scans are first filtered out through a high rate extended Kalman Filter (EKF) to remove the effect of short-term noise associated with LiDAR scans. Then the smoothed orientation and range changes are fused by a low rate EKF with those predicted by gyroscope and wheel encoder. The proposed system is verified through real experiment on a wirelessly controlled Unmanned Ground Vehicle (UGV). Experimental results indicate that navigation accuracy has been improved to sub-meter and gyroscope bias is precisely estimated.

The two main categories of light detection and ranging (LIDAR) for mapping and monitoring landslides are static and mobile devices. Static devices are commonly named ground-based LIDAR or terrestrial laser scanners (TLSs). Regarding mobile devices, the different instruments are normally classified depending on the platform configuration: either boats, cars or flying devices. There are three main types of LIDAR, corresponding to three different ways of measuring the range, for instance, the distance along the line of sight (LOS) between the sensor and the terrain. The types include: pulse LIDAR measures the time of flight (TOF) of a laser pulse; phase LIDAR uses the phase shift between the emitted and received signals; and triangulation LIDAR uses a camera to locate the laser spot on the scanned surface. Georeferencing is the process of coordinate transformation from an internal referential system relative to the LIDAR device into a real-world system of reference.

Light Detection and Ranging (LIDAR) scanning can be used to safely and remotely provide intelligence on the interior of dangerous structures for use by first responders that need to enter these structures. By scanning into structures through windows and other openings or moving the LIDAR scanning into the structure, in both cases carried by a remote controlled robotic crawler, the presence of dangerous items or personnel can be confirmed or denied. Entry and egress pathways can be determined in advance, and potential hiding/ambush locations identified. This paper describes an integrated system of a robotic crawler and LIDAR scanner. Both the scanner and the robot are wirelessly remote controlled from a single laptop computer. This includes navigation of the crawler with real-time video, self-leveling of the LIDAR platform, and the ability to raise the scanner up to heights of 2.5 m. Multiple scans can be taken from different angles to fill in detail and provide more complete coverage. These scans can quickly be registered to each other using user defi ned 'pick points', creating a single point cloud from multiple scans. Software has been developed to deconstruct the point clouds, and identify specific objects in the interior of the structure from the point cloud. Software has been developed to interactively visualize and walk through the modeled structures. Floor plans are automatically generated and a data export facility has been developed. Tests have been conducted on multiple structures, simulating many of the contingencies that a first responder would face. Keywords Hazards, laser scanning, LIDAR, model, segmentation Language: en

&lt;span lang="EN-US"&gt;In recent years, there has been a strong demand for self-driving cars. For safe navigation, self-driving cars need both precise localization and robust mapping. While global navigation satellite system (GNSS) can be used to locate vehicles, it has some limitations, such as satellite signal absence (tunnels and caves), which restrict its use in urban scenarios. Simultaneous localization and mapping (SLAM) are an excellent solution for identifying a vehicle’s position while at the same time constructing a representation of the environment. SLAM-based visual and light detection and ranging (LIDAR) refer to using cameras and LIDAR as source of external information. This paper presents an implementation of SLAM algorithm for building a map of environment and obtaining car’s trajectory using LIDAR scans. A detailed overview of current visual and LIDAR SLAM approaches has also been provided and discussed. Simulation results referred to LIDAR scans indicate that SLAM is convenient and helpful in localization and mapping.&lt;/span&gt;

Water leakage mapping in concrete railway tunnels using LiDAR generated point clouds

This paper reports on a fast multiresolution scan matcher for local vehicle localization of self-driving cars. State-of-the-art approaches to vehicle localization rely on observing road surface reflectivity with a 3D light detection and ranging (LIDAR) scanner to achieve centimeter-level accuracy. However, these approaches can often fail when faced with adverse weather conditions that obscure the view of the road paint (e.g. puddles and snowdrifts), poor road surface texture, or when road appearance degrades over time. We present a generic probabilistic method for localizing an autonomous vehicle equipped with a three-dimensional (3D) LIDAR scanner. This proposed algorithm models the world as a mixture of several Gaussians, characterizing the -height and reflectivity distribution of the environment—which we rasterize to facilitate fast and exact multiresolution inference. Results are shown on a collection of datasets totaling over 500 km of road data covering highway, rural, residential, and urban roadways, in which we demonstrate our method to be robust through heavy snowfall and roadway repavements.

Aerial light detection and ranging (LiDAR) offers the potential to autogenerate detailed, three-dimensional (3D) models of the built environment in urban settings. Autogeneration is needed as manual generation is not economically feasible for large areas, and such models are needed for a wide range of applications from improved noise and pollution prediction to disaster mitigation modeling and visualization. Current laser scanning hardware and the dense geometry of urban environments are two major constraints in LiDAR scanning. This paper outlines the difficulties related to effective surface data capture, with emphasis on vertical surfaces, in an urban environment for the purpose of 3D modeling. A flight planning strategy to overcome these difficulties is presented, along with a case study of a data set collected with this strategy. The main conclusions of this study are that an appropriate amount of strip overlap, together with a flight path diagonal to the underlying street grid produces a vastly enhanced level of detail on vertical surfaces, beyond what has been previously available.

In this study, we describe the realization of a time-of-flight scanning LiDAR (Light Detection and Ranging) prototype, that leverages mirrors as micro-electromechanical systems (MEMS) for agile beam steering in a compact size, and a field-programmable gate array (FPGA)-based processing unit for real-time 3D image reconstruction. The proposed 3D LiDAR system is designed to operate within a range of up to 1 meter with a spatial resolution of 400 × 300 pixels at a frame rate of 30 Hz. The LiDAR prototype architecture consists of 3 main parts: an optomechanical system, a digital processing unit (FPGA-based), an analog front-end. Processed 3D depth maps are rendered in real time via a high-definition multimedia interface (HDMI), providing immediate visual feedback. The full system was deeply characterized and tested. The integration of MEMS mirrors, an FPGA-based time-to-digital converter, and an optimized analog front-end resulted in a highly efficient, compact, and real-time depth sensing platform, ready for a final engineering step. The results obtained represent a feasibility study for a potential commercial product, that is low-cost and small-size, with different consumer applications.

This paper suggests a MEMS-based indirect time-of-flight (ToF) scanning light detection and ranging (LiDAR) system with parallel-phase demodulation. Based on the parallel-phase demodulation which extremely reduces the integration time maintaining high demodulation contrast, the proposed LiDAR can acquire accurate depth images with mean absolute error (MAE) about 1.5 cm at the distance of 1.85 m using 20 mW laser power. Meanwhile, MAE due to multipath interference (MPI) of the proposed LiDAR originally about 1.5 cm could be further reduced to less than 8 mm using support vector regression (SVR).

Device-free localization (DFL) is a technique of locating targets via wireless sensor networks (WSNs) without carrying any electronic devices. This article introduces a device-free vehicle localization system that can determine the position and 2-D size of a dynamic vehicle in a monitoring region via ultra wide band (UWB) networks. Specifically, our system involves an obstructed recognition model for detecting the state of links, a state-input radio tomography imaging (SI-RTI) algorithm for reconstructing a high-resolution image, a contour threshold method for predicting the size of a vehicle target, and a gradient-advance-retreat (GAR) optimization algorithm for improving accuracy. Field experiments show that our system can estimate the position and size of a moving vehicle with a location root-mean-square error (RMSE) of less than 1.1 m and a root-mean-square overlap degree of more than 75% between the prediction and the real target.

&amp;lt;p&amp;gt;The interest in indoor positioning systems has been rising in the last couple of decades. This interest was further accelerated by the availability of signals of opportunity. The introduction of signals of opportunity into the indoor environments has meant that these widely available technologies can be utilised to achieve higher levels of accuracy and precision for positioning in parking garages, airports, underground locations, large office buildings, and shopping centres. Indoor positioning can also be utilised for emergency services or for Building Information Models (BIM) for purposes such as construction sites or hospital building management. In addition to the need for indoor and outdoor positioning, there is a need to ensure seamless transitions in and out of these different environments. For example, building information from BIM could be utilised for navigation of BIM users that considers the architectural barriers (e.g., for wheelchairs, baby strollers, and transport of large equipment within the facility). For this, a positioning system capable of seamlessly transitioning in and out of buildings is needed. This paper proposes a multi-sensor and cooperative positioning method of seamless environment transition and presents the first architecture concept. For this purpose, Global Navigation Satellite System (GNSS) data are planned to be fused with Ultra Wide-Band (UWB), Inertial Measurement Unit (IMU), Wireless-Fidelity (Wi-Fi), visual camera and LiDAR (Light Detection and Ranging) data. All sensors required for such a positioning system, are already available on modern smartphones commonly used as low-cost platforms for pedestrian navigation and localisation. Newer smartphones are also equipped with LiDAR and UWB chips, which are a future signal of opportunity, just like Wi-Fi currently is. The transition to different environments will be determined based on the UWB anchors called identical anchors that will be used to transition from the global coordinate system for the outdoor environment to the local building environment for positioning in indoor environments. The proposed multi-sensor and cooperative positioning system is expected to be able to achieve decimetre-level accuracy.&amp;lt;/p&amp;gt;