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

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