Scan-Matching based Particle Filtering approach for LIDAR-only Localization

This paper deals with the development of a localization methodology for autonomous vehicles using only a 3\Dim LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is essentially to find its global 3\Dim pose (position and orientation) within this map. The problem of tracking is then to use sequential LIDAR scan measurement to also estimate other states such as velocity and angular rates, in addition to the global pose of the vehicle. Particle filters are often used in localization and tracking, as in applications of simultaneously localization and mapping. But particle filters become computationally prohibitive with the increase in particles, often required to localize in a large 3\Dim map. Further, computing the likelihood of a LIDAR scan for each particle is in itself a computationally expensive task, thus limiting the number of particles that can be used for real time performance. To this end, we propose a hybrid approach that combines the advantages of a particle filter with a global-local scan matching method to better inform the re-sampling stage of the particle filter. Further, we propose to use a pre-computed likelihood grid to speedup the computation of LIDAR scans. Finally, we develop the complete algorithm to extensively leverage parallel processing to achieve near sufficient real-time performance on publicly available KITTI datasets

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research.close

Scan Matching-Based Particle Filter for LIDAR-Only Localization

This paper deals with the development of a localization methodology for autonomous vehicles using only a 3D LIDAR sensor. In the context of this paper, localizing a vehicle in a known 3D global map of the environment is equivalent to finding the vehicle’s global 3D pose (position and orientation), in addition to other vehicle states, within this map. Once localized, the problem of tracking uses the sequential LIDAR scans to continuously estimate the states of the vehicle. While the proposed scan matching-based particle filters can be used for both localization and tracking, in this paper, we emphasize only the localization problem. Particle filters are a well-known solution for robot/vehicle localization, but they become computationally prohibitive as the states and the number of particles increases. Further, computing the likelihood of a LIDAR scan for each particle is in itself a computationally expensive task, thus limiting the number of particles that can be used for real-time performance. To this end, a hybrid approach is proposed that combines the advantages of a particle filter with a global-local scan matching method to better inform the resampling stage of the particle filter. We also use a pre-computed likelihood grid to speed up the computation of LIDAR scan likelihoods. Using simulation data of real-world LIDAR scans from the KITTI datasets, we show the efficacy of the proposed approach

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Networking research often relies on simulations to design, analyze and test various protocols and mechanisms. These simulations should be performed in environments similar to the ones in which the protocols will be deployed. As such, the workload with which these protocols are tested in the simulation environment should match the properties of the traffic that is encountered in the real network. Source-level modeling of traffic is one in which a set of applications, or a set of users, is simulated to generate traffic that is characteristically similar to the traffic found on real network. This type of modeling helps in conducting realistic simulations. The source-level a-b-t model and accompanying tmix traffic generator for testbeds was developed at the University of North Carolina by F. Hernandez Campos. We have implemented this tmix traffic generator in the popular ns-2 and GTNetS network simulators. We show that our module can replay the connection vectors generated by the a-b-t model and generate workloads that are characteristically similar to the traffic found on a real internet link. In this thesis we describe in detail the design and implementation of our tmix module in ns-2 an

Tmix: A Tool for Generating Realistic TCP Application Workloads in ns-2

In order to perform realistic network simulations, one needs a traffic generator that is capable of generating realistic synthetic traffic in a closed-loop fashion that “looks like ” traffic found on an actual network. We describe such a traffic generation system for the widely used ns-2 simulator. The system takes as input a packet header trace taken from a network link of interest. The trace is “reverse compiled ” into a source-level characterization of each TCP connection present in the trace. The characterization, called a connection vector, is then used as input to an ns module called tmix that emulates the socket-level behavior of the source application that created the corresponding connection in the trace. This emulation faithfully reproduces the essential pattern of socket reads and writes that the original application performed without knowledge of what the original application actually was. When combined with a network path emulation component we have constructed called DelayBox, the resulting traffic generated in the simulation is statistically representative of the traffic measured on the real link. This approach to synthetic traffic generation allows one to automatically repro-duce in ns the full range of TCP connections found on an arbitrary link. Thus with our tools, researchers no longer need make arbitrary decisions on how traffic is generated in simulations and can instead easily generate TCP traffic that represents the use of a net-work by the full mix of applications measured on actual network links of interest. The method is evaluated by applying it to packet header traces taken from campus and wide-area networks and comparing the statistical properties of traffic on the measured links with traffic generated by tmix in ns

Tmix: A Tool for Generating Realistic TCP Application Workloads in ns-2

In order to perform realistic network simulations, one needs a traffic generator that is capable of generating realistic synthetic traffic in a closed-loop fashion that "looks like" traffic found on an actual network. We describe such a traffic generation system for the widely used ns-2 simulator. The system takes as input a pa ket header tra e taken from a network link of interest. The tra e is "reverse ompiled" into a sour e-level hara terization of ea h TCP onne tion present in the tra e. The hara terization, alled a onne tion ve tor, is then used as input to an ns module alled tmix that emulates the so ket-level behavior of the sour e appli ation that reated the orresponding onne tion in the tra e. This emulation faithfully reprodu es the essential pattern of so ket reads and writes that the original appli ation performed without knowledge of what the original appli ation a tually was. When ombined with a network path emulation omponent we have onstru ted alled DelayBox, the resulting traffi generated in the simulation is statisti ally representative of the traffi measured on the real link. This approa h to syntheti traffi generation allows one to automatically reproduce in ns the full range of TCP connections found on an arbitrary link. Thus with our tools, resear hers no longer need make arbitrary decisions on how traffic is generated in simulations and an instead easily generate TCP traffic that represents the use of a net-work by the full mix of applications measured on a tual network links of interest. The method is evaluated by applying it to pa ket header tra es taken from ampus and wide-area networks and omparing the statisti al properties of traffi on the measured links with traffic generated by tmix in ns