Accurate large-scale bearing-only SLAM by map joining

This paper presents a bearing-only SLAM algorithm that generates accurate and consistent maps of large environments by joining a series of small local maps. The local maps are built by least squares optimization with a proper landmark initialization technique. The local maps are then combined to build global map using Iterated Sparse Local Submap Joining Filter (I-SLSJF). The accuracy and consistency of the proposed algorithm is evaluated using simulation data sets. The algorithm is also tested using the DLR-Spatial-Cognition data set and the preprocessed Victoria Park data where the range information is ignored. The global map results are very similar to the result of full least squares optimization starting with very accurate initial values. As I-SLSJF is able to join a given set of local maps and associated uncertainties efficiently without any information loss, these results demonstrate that focusing on generating accurate local maps is a promising direction for solving large-scale bearing-only SLAM problems

Inertial navigation aided by simultaneous loacalization and mapping

Unmanned aerial vehicles technologies are getting smaller and cheaper to use and the challenges of payload limitation in unmanned aerial vehicles are being overcome. Integrated navigation system design requires selection of set of sensors and computation power that provides reliable and accurate navigation parameters (position, velocity and attitude) with high update rates and bandwidth in small and cost effective manner. Many of today’s operational unmanned aerial vehicles navigation systems rely on inertial sensors as a primary measurement source. Inertial Navigation alone however suffers from slow divergence with time. This divergence is often compensated for by employing some additional source of navigation information external to Inertial Navigation. From the 1990’s to the present day Global Positioning System has been the dominant navigation aid for Inertial Navigation. In a number of scenarios, Global Positioning System measurements may be completely unavailable or they simply may not be precise (or reliable) enough to be used to adequately update the Inertial Navigation hence alternative methods have seen great attention. Aiding Inertial Navigation with vision sensors has been the favoured solution over the past several years. Inertial and vision sensors with their complementary characteristics have the potential to answer the requirements for reliable and accurate navigation parameters. In this thesis we address Inertial Navigation position divergence. The information for updating the position comes from combination of vision and motion. When using such a combination many of the difficulties of the vision sensors (relative depth, geometry and size of objects, image blur and etc.) can be circumvented. Motion grants the vision sensors with many cues that can help better to acquire information about the environment, for instance creating a precise map of the environment and localize within the environment. We propose changes to the Simultaneous Localization and Mapping augmented state vector in order to take repeated measurements of the map point. We show that these repeated measurements with certain manoeuvres (motion) around or by the map point are crucial for constraining the Inertial Navigation position divergence (bounded estimation error) while manoeuvring in vicinity of the map point. This eliminates some of the uncertainty of the map point estimates i.e. it reduces the covariance of the map points estimates. This concept brings different parameterization (feature initialisation) of the map points in Simultaneous Localization and Mapping and we refer to it as concept of aiding Inertial Navigation by Simultaneous Localization and Mapping. We show that making such an integrated navigation system requires coordination with the guidance and control measurements and the vehicle task itself for performing the required vehicle manoeuvres (motion) and achieving better navigation accuracy. This fact brings new challenges to the practical design of these modern jam proof Global Positioning System free autonomous navigation systems. Further to the concept of aiding Inertial Navigation by Simultaneous Localization and Mapping we have investigated how a bearing only sensor such as single camera can be used for aiding Inertial Navigation. The results of the concept of Inertial Navigation aided by Simultaneous Localization and Mapping were used. New parameterization of the map point in Bearing Only Simultaneous Localization and Mapping is proposed. Because of the number of significant problems that appear when implementing the Extended Kalman Filter in Inertial Navigation aided by Bearing Only Simultaneous Localization and Mapping other algorithms such as Iterated Extended Kalman Filter, Unscented Kalman Filter and Particle Filters were implemented. From the results obtained, the conclusion can be drawn that the nonlinear filters should be the choice of estimators for this application

Visual-Inertial Mapping with Non-Linear Factor Recovery

Cameras and inertial measurement units are complementary sensors for ego-motion estimation and environment mapping. Their combination makes visual-inertial odometry (VIO) systems more accurate and robust. For globally consistent mapping, however, combining visual and inertial information is not straightforward. To estimate the motion and geometry with a set of images large baselines are required. Because of that, most systems operate on keyframes that have large time intervals between each other. Inertial data on the other hand quickly degrades with the duration of the intervals and after several seconds of integration, it typically contains only little useful information. In this paper, we propose to extract relevant information for visual-inertial mapping from visual-inertial odometry using non-linear factor recovery. We reconstruct a set of non-linear factors that make an optimal approximation of the information on the trajectory accumulated by VIO. To obtain a globally consistent map we combine these factors with loop-closing constraints using bundle adjustment. The VIO factors make the roll and pitch angles of the global map observable, and improve the robustness and the accuracy of the mapping. In experiments on a public benchmark, we demonstrate superior performance of our method over the state-of-the-art approaches

Concurrent Initialization for Bearing-Only SLAM

Simultaneous Localization and Mapping (SLAM) is perhaps the most fundamental problem to solve in robotics in order to build truly autonomous mobile robots. The sensors have a large impact on the algorithm used for SLAM. Early SLAM approaches focused on the use of range sensors as sonar rings or lasers. However, cameras have become more and more used, because they yield a lot of information and are well adapted for embedded systems: they are light, cheap and power saving. Unlike range sensors which provide range and angular information, a camera is a projective sensor which measures the bearing of images features. Therefore depth information (range) cannot be obtained in a single step. This fact has propitiated the emergence of a new family of SLAM algorithms: the Bearing-Only SLAM methods, which mainly rely in especial techniques for features system-initialization in order to enable the use of bearing sensors (as cameras) in SLAM systems. In this work a novel and robust method, called Concurrent Initialization, is presented which is inspired by having the complementary advantages of the Undelayed and Delayed methods that represent the most common approaches for addressing the problem. The key is to use concurrently two kinds of feature representations for both undelayed and delayed stages of the estimation. The simulations results show that the proposed method surpasses the performance of previous schemes

Batch Nonlinear Continuous-Time Trajectory Estimation as Exactly Sparse Gaussian Process Regression

In this paper, we revisit batch state estimation through the lens of Gaussian process (GP) regression. We consider continuous-discrete estimation problems wherein a trajectory is viewed as a one-dimensional GP, with time as the independent variable. Our continuous-time prior can be defined by any nonlinear, time-varying stochastic differential equation driven by white noise; this allows the possibility of smoothing our trajectory estimates using a variety of vehicle dynamics models (e.g., `constant-velocity'). We show that this class of prior results in an inverse kernel matrix (i.e., covariance matrix between all pairs of measurement times) that is exactly sparse (block-tridiagonal) and that this can be exploited to carry out GP regression (and interpolation) very efficiently. When the prior is based on a linear, time-varying stochastic differential equation and the measurement model is also linear, this GP approach is equivalent to classical, discrete-time smoothing (at the measurement times); when a nonlinearity is present, we iterate over the whole trajectory to maximize accuracy. We test the approach experimentally on a simultaneous trajectory estimation and mapping problem using a mobile robot dataset.Comment: Submitted to Autonomous Robots on 20 November 2014, manuscript # AURO-D-14-00185, 16 pages, 7 figure

A Comprehensive Introduction of Visual-Inertial Navigation

In this article, a tutorial introduction to visual-inertial navigation(VIN) is presented. Visual and inertial perception are two complementary sensing modalities. Cameras and inertial measurement units (IMU) are the corresponding sensors for these two modalities. The low cost and light weight of camera-IMU sensor combinations make them ubiquitous in robotic navigation. Visual-inertial Navigation is a state estimation problem, that estimates the ego-motion and local environment of the sensor platform. This paper presents visual-inertial navigation in the classical state estimation framework, first illustrating the estimation problem in terms of state variables and system models, including related quantities representations (Parameterizations), IMU dynamic and camera measurement models, and corresponding general probabilistic graphical models (Factor Graph). Secondly, we investigate the existing model-based estimation methodologies, these involve filter-based and optimization-based frameworks and related on-manifold operations. We also discuss the calibration of some relevant parameters, also initialization of state of interest in optimization-based frameworks. Then the evaluation and improvement of VIN in terms of accuracy, efficiency, and robustness are discussed. Finally, we briefly mention the recent development of learning-based methods that may become alternatives to traditional model-based methods.Comment: 35 pages, 10 figure

Simultaneous localisation and mapping: A stereo vision based approach

With limited dynamic range and poor noise performance, cameras still pose considerable challenges in the application of range sensors in the context of robotic navigation, especially in the implementation of Simultaneous Localisation and Mapping (SLAM) with sparse features. This paper presents a combination of methods in solving the SLAM problem in a constricted indoor environment using small baseline stereo vision. Main contributions include a feature selection and tracking algorithm, a stereo noise filter, a robust feature validation algorithm and a multiple hypotheses adaptive window positioning method in 'closing the loop'. These methods take a novel approach in that information from the image processing and robotic navigation domains are used in tandem to augment each other. Experimental results including a real-time implementation in an office-like environment are also presented. © 2006 IEEE

Real-Time Multi-Fisheye Camera Self-Localization and Egomotion Estimation in Complex Indoor Environments

In this work a real-time capable multi-fisheye camera self-localization and egomotion estimation framework is developed. The thesis covers all aspects ranging from omnidirectional camera calibration to the development of a complete multi-fisheye camera SLAM system based on a generic multi-camera bundle adjustment method