Accurate SLAM With Application For Aerial Path Planning

This thesis focuses on operation of Micro Aerial Vehicles (MAVs), in previously unexplored, GPS-denied environments. For this purpose, a refined Simultaneous Localization And Mapping (SLAM) algorithm using a laser range scanner is developed, capable of producing a map of the traversed environment, and estimating the position of the MAV within the evolving map. The algorithm's accuracy is quantitatively assessed using several dedicated metrics, showing significant advantages over current methods. Repeatability and robustness are shown using a set of 12 repeated experiments in a benchmark scenario. The SLAM algorithm is primarily based on an innovative scan matching approach, dubbed Perimeter Based Polar Scan Matching (PB-PSM), which introduces a maximum overlap term to the cost function. This term, along with a tailored cost minimization technique, are found to yield highly accurate solutions for scan matching pairs of range scans. The algorithm is extensively tested on both ground and aerial platforms, in indoor as well as outdoor scenarios, using both in-house and previously published datasets, utilizing several different laser scanners. The SLAM algorithm is then coupled with a global A* path planner, and applied on a single rotor helicopter, performing targeted flight missions using a pilot-in-the- loop implementation. Targeted flight is defined as navigating to a goal position, defined by relative distance from a known initial position. It differs from the more common task of mapping, as it may not rely on loop closure opportunities to smooth out errors and optimize the generated map. Therefore, the importance of position estimates accuracy increases dramatically. The complete algorithm is then used for targeted flight experiments with a pilot in the loop. The algorithm presents the pilot with nothing but heading information. In order to further prevent the pilot from interfering with the obstacle avoidance task, the evolving map and position are not shown to the human pilot. Furthermore, the scenario is introduced with artificial (invisible) obstacles, apparent only to the path planner. The pilot therefore has to adhere to the path planner directions in order to reach the goal while avoiding all obstacles. The resulting paths show the helicopter successfully avoid both real and artificial obstacles, while following the planned path to the goal

Scan matching by cross-correlation and differential evolution

Scan matching is an important task, solved in the context of many high-level problems including pose estimation, indoor localization, simultaneous localization and mapping and others. Methods that are accurate and adaptive and at the same time computationally efficient are required to enable location-based services in autonomous mobile devices. Such devices usually have a wide range of high-resolution sensors but only a limited processing power and constrained energy supply. This work introduces a novel high-level scan matching strategy that uses a combination of two advanced algorithms recently used in this field: cross-correlation and differential evolution. The cross-correlation between two laser range scans is used as an efficient measure of scan alignment and the differential evolution algorithm is used to search for the parameters of a transformation that aligns the scans. The proposed method was experimentally validated and showed good ability to match laser range scans taken shortly after each other and an excellent ability to match laser range scans taken with longer time intervals between them.Web of Science88art. no. 85

Novel point-to-point scan matching algorithm based on cross-correlation

The localization of mobile robots in outdoor and indoor environments is a complex issue. Many sophisticated approaches, based on various types of sensory inputs and different computational concepts, are used to accomplish this task. However, many of the most efficient methods for mobile robot localization suffer from high computational costs and/or the need for high resolution sensory inputs. Scan cross-correlation is a traditional approach that can be, in special cases, used to match temporally aligned scans of robot environment. This work proposes a set of novel modifications to the cross-correlation method that extend its capability beyond these special cases to general scan matching and mitigate its computational costs so that it is usable in practical settings. The properties and validity of the proposed approach are in this study illustrated on a number of computational experiments.Web of Scienceart. ID 646394

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp