Tradeoffs in SLAM with sparse information filters

Designing filters exploiting the sparseness of the information matrix for efficiently solving the simultaneous localization and mapping (SLAM) problem has attracted significant attention during the recent past. The main contribution of this paper is a review of the various sparse information filters proposed in the literature to date, in particular, the compromises used to achieve sparseness. Two of the most recent algorithms that the authors have implemented, Exactly Sparse Extended Information Filter (ESEIF) by Walter et al. [5] and the D-SLAM by Wang et al. [6] are discussed and analyzed in detail. It is proposed that this analysis can stimulate developing a framework suitable for evaluating the relative merits of SLAM algorithms. © 2008 Springer-Verlag Berlin Heidelberg

Iterated D-SLAM map joining: Evaluating its performance in terms of consistency, accuracy and efficiency

This paper presents a new map joining algorithm and a set of metrics for evaluating the performance of mapping techniques. The input to the new map joining algorithm is a sequence of local maps containing the feature positions and the final robot pose in a local frame of reference. The output is a global map containing the global positions of all the features but without any robot poses. The algorithm is built on the D-SLAM mapping algorithm (Wang et al. in Int. J. Robot. Res. 26(2):187-204, 2007) and uses iterations to improve the estimates in the map joining step. So it is called Iterated D-SLAM Map Joining (I-DMJ). When joining maps I-DMJ ignores the odometry information connecting successive maps. This is the key to I-DMJ efficiency, because it makes both the information matrix exactly sparse and the size of the state vector bounded by the number of features. The paper proposes metrics for quantifying the performance of different mapping algorithms focusing on evaluating their consistency, accuracy and efficiency. The I-DMJ algorithm and a number of existing SLAM algorithms are evaluated using the proposed metrics. The simulation data sets and a preprocessed Victoria Park data set used in this paper are made available to enable interested researchers to compare their mapping algorithms with I-DMJ. © 2009 Springer Science+Business Media, LLC

Towards Collaborative Simultaneous Localization and Mapping: a Survey of the Current Research Landscape

Motivated by the tremendous progress we witnessed in recent years, this paper presents a survey of the scientific literature on the topic of Collaborative Simultaneous Localization and Mapping (C-SLAM), also known as multi-robot SLAM. With fleets of self-driving cars on the horizon and the rise of multi-robot systems in industrial applications, we believe that Collaborative SLAM will soon become a cornerstone of future robotic applications. In this survey, we introduce the basic concepts of C-SLAM and present a thorough literature review. We also outline the major challenges and limitations of C-SLAM in terms of robustness, communication, and resource management. We conclude by exploring the area's current trends and promising research avenues.Comment: 44 pages, 3 figure

Faster Algorithms for Weighted Recursive State Machines

Pushdown systems (PDSs) and recursive state machines (RSMs), which are linearly equivalent, are standard models for interprocedural analysis. Yet RSMs are more convenient as they (a) explicitly model function calls and returns, and (b) specify many natural parameters for algorithmic analysis, e.g., the number of entries and exits. We consider a general framework where RSM transitions are labeled from a semiring and path properties are algebraic with semiring operations, which can model, e.g., interprocedural reachability and dataflow analysis problems. Our main contributions are new algorithms for several fundamental problems. As compared to a direct translation of RSMs to PDSs and the best-known existing bounds of PDSs, our analysis algorithm improves the complexity for finite-height semirings (that subsumes reachability and standard dataflow properties). We further consider the problem of extracting distance values from the representation structures computed by our algorithm, and give efficient algorithms that distinguish the complexity of a one-time preprocessing from the complexity of each individual query. Another advantage of our algorithm is that our improvements carry over to the concurrent setting, where we improve the best-known complexity for the context-bounded analysis of concurrent RSMs. Finally, we provide a prototype implementation that gives a significant speed-up on several benchmarks from the SLAM/SDV project

Simultaneous Localization and Mapping with Power Network Electromagnetic Field

Various sensing modalities have been exploited for indoor location sensing, each of which has well understood limitations, however. This paper presents a first systematic study on using the electromagnetic field (EMF) induced by a building's electric power network for simultaneous localization and mapping (SLAM). A basis of this work is a measurement study showing that the power network EMF sensed by either a customized sensor or smartphone's microphone as a side-channel sensor is spatially distinct and temporally stable. Based on this, we design a SLAM approach that can reliably detect loop closures based on EMF sensing results. With the EMF feature map constructed by SLAM, we also design an efficient online localization scheme for resource-constrained mobiles. Evaluation in three indoor spaces shows that the power network EMF is a promising modality for location sensing on mobile devices, which is able to run in real time and achieve sub-meter accuracy

The Advantage of Custom Microprocessors for Stochastic Gradient Descent in Graph-Based Robot Localization and Mapping

Simultaneous Localization and Mapping (SLAM) describes a class of problems facing a large and growing field of autonomous systems -- from self-driving cars, to interplanetary rovers, to home automation products. Unfortunately this is a complex task where sophisticated algorithms and data structures are required to navigate a wide range of uncharted environments. Furthermore, most mobile robots need to run these tasks near real-time onboard an embedded controller with limited power and compute resources. To address this problem we explore the stochastic gradient descent (SGD) variant of graph solvers for SLAM and observe a tradeoff between various execution architectures and overall execution speed. Based on these observations, we propose a custom multiprocessor design that relaxes memory-coherency constraints between parallel cores while avoiding divergent behavior. We introduce a specialized streaming-tree interconnect that provides increased performance while using fewer resources compared to state-of-art GPU/CPU implementations of SGD. Finally, we discuss applications of unconventional architectural paradigms like over-provisioned “dark processors” and specialized data partitioning that provided a unique performance advantage for our particular design

Efficient and Featureless Approaches to Bathymetric Simultaneous Localisation and Mapping

This thesis investigates efficient forms of Simultaneous Localization and Mapping (SLAM) that do not require explicit identification, tracking or association of map features. The specific application considered here is subsea robotic bathymetric mapping. In this context, SLAM allows a GPS-denied robot operating near the sea floor to create a self-consistent bathymetric map. This is accomplished using a Rao-Blackwellized Particle Filter (RBPF) whereby each particle maintains a hypothesis of the current vehicle state and map that is efficiently maintained using Distributed Particle Mapping. Through particle weighting and resampling, successive observations of the seafloor structure are used to improve the estimated trajectory and resulting map by enforcing map self consistency. The main contributions of this thesis are two novel map representations, either of which can be paired with the RBPF to perform SLAM. The first is a grid-based 2D depth map that is efficiently stored by exploiting redundancies between different maps. The second is a trajectory map representation that, instead of directly storing estimates of seabed depth, records the trajectory of each particle and synchronises it to a common log of bathymetric observations. Upon detecting a loop closure each particle is weighted by matching new observations to the current predictions. For the grid map approach this is done by extracting the predictions stored in the observed cells. For the trajectory map approach predictions are instead generated from a local reconstruction of their map using Gaussian Process Regression. While the former allows for faster map access the latter requires less memory and fully exploits the spatial correlation in the environment, allowing predictions of seabed depth to be generated in areas that were not directly observed previously. In this case particle resampling therefore not only enforces self-consistency in overlapping sections of the map but additionally enforces self-consistency between neighboring map borders. Both approaches are validated using multibeam sonar data collected from several missions of varying scale by a variety of different Unmanned Underwater Vehicles. These trials demonstrate how the corrections provided by both approaches improve the trajectory and map when compared to dead reckoning fused with Ultra Short Baseline or Long Baseline observations. Furthermore, results are compared with a pre-existing state of the art bathymetric SLAM technique, confirming that similar results can be achieved at a fraction of the computation cost. Lastly the added capabilities of the trajectory map are validated using two different bathymetric datasets. These demonstrate how navigation and mapping corrections can still be achieved when only sparse bathymetry is available (e.g. from a four beam Doppler Velocity Log sensor) or in missions where map overlap is minimal or even non-existent

Multi-SLAM Systems for Fault-Tolerant Simultaneous Localization and Mapping

Mobile robots need accurate, high fidelity models of their operating environments in order to complete their tasks safely and efficiently. Generating these models is most often done via Simultaneous Localization and Mapping (SLAM), a paradigm where the robot alternatively estimates the most up-to-date model of the environment and its position relative to this model as it acquires new information from its sensors over time. Because robots operate in many different environments with different compute, memory, sensing, and form constraints, the nature and quality of information available to individual instances of different SLAM systems varies substantially. `One-size-fits-all\u27 solutions are thus exceedingly difficult to engineer, and highly specialized systems, which represent the state-of-the-art for most types of deployments, are not robust to operating conditions in which their assumptions are not met. This thesis seeks to investigate an alternative approach to these robustness and universality problems by incorporating existing SLAM solutions within a larger framework supported by planning and learning. The central idea is to combine learned models that estimate SLAM algorithm performance under a variety of sensory conditions, in this case neural networks, with planners designed for planning under uncertainty and partial observability, in this case partially observable Markov decision problems (POMDPs). Models of existing SLAM algorithms can be learned, and these models can then be used online to estimate the performance of a range of solutions to the SLAM problem at hand. The POMDP policy then selects the appropriate algorithm, given the estimated performance, cost of switching methods, and other information. This general approach may also be applicable to many other robotics problems that rely on data-fusion, such as grasp planning, motion planning, or object identification

Computationally-Efficient Visual-Inertial Simultaneous Localization and Mapping for Spaceflight Navigation

This thesis represents an investigation into the application to spaceflight of the estimation techniques developed to solve the well-known robotics problem of Simultaneous Localization and Mapping (SLAM). This subject has been thoroughly studied in the context of ground and aerial robotics but its study for use in the spaceflight domain, where the dynamical, measurement, and computational challenges are often very different than in terrestrial applications, is less common. Further, the wide body of extant robotics research into SLAM can be difficult to approach and understand for space navigators with a more classical estimation background because of the differences in terminology and assumptions that roboticists have utilized over time. This work offers an overview of the development of SLAM in robotics and how it has been applied in that field, as well as an accessible approach to the problem for researchers with an aerospace background. This also leads into the development of a novel visual-inertial (VI) SLAM algorithm designed to achieve constant-time exploration and mapping while still integrating the full nonlinear dynamics of the space environment, handling the high update rates of inertial measurement units (IMUs), and incorporating the measurement information produced by a camera sensor. This algorithm is applied to 2D and 3D simulated and real datasets to demonstrate its capability to quickly generate accurate state estimates of both spacecraft and environmental variables.</p

Review of Neurobiologically Based Mobile Robot Navigation System Research Performed Since 2000

In an attempt to better understand how the navigation part of the brain works and to possibly create smarter and more reliable navigation systems, many papers have been written in the field of biomimetic systems. This paper presents a literature survey of state-of-the-art research performed since the year 2000 on rodent neurobiological and neurophysiologically based navigation systems that incorporate models of spatial awareness and navigation brain cells. The main focus is to explore the functionality of the cognitive maps developed in these mobile robot systems with respect to route planning, as well as a discussion/analysis of the computational complexity required to scale these systems.http://dx.doi.org/10.1155/2016/863725