Search

Roboticists often formulate estimation problems in discrete time for the practical reason of keeping the state size tractable; however, the discrete-time approach does not scale well for use with high-rate sensors, such as inertial measurement units, rolling-shutter cameras, or sweeping laser imaging sensors. The difficulty lies in the fact that a pose variable is typically included for every time at which a measurement is acquired, rendering the dimension of the state impractically large for large numbers of measurements. This issue is exacerbated for the simultaneous localization and mapping problem, which further augments the state to include landmark variables. To address this tractability issue, we propose to move the full Maximum-a-Posteriori estimation problem into continuous time and use temporal basis functions to keep the state size manageable. We present a full probabilistic derivation of the continuous-time estimation problem, derive an estimator based on the assumption that the densities and processes involved are Gaussian and show how the coefficients of a relatively small number of basis functions can form the state to be estimated, making the solution efficient. Our derivation is presented in steps of increasingly specific assumptions, opening the door to the development of other novel continuous-time estimation algorithms through the application of different assumptions at any point. We use the simultaneous localization and mapping problem as our motivation throughout the paper, although the approach is not specific to this application. Results from two experiments are provided to validate the approach: (i) self-calibration involving a camera and a high-rate inertial measurement unit, and (ii) perspective localization with a rolling-shutter camera. [ABSTRACT FROM AUTHOR]

This paper is concerned with real-time monocular visual–inertial simultaneous localization and mapping (SLAM). In particular a tightly coupled nonlinear-optimization-based solution that can match the global optimal result in real time is proposed. The methodology is motivated by the requirement to produce a scale-correct visual map, in an optimization framework that is able to incorporate relocalization and loop closure constraints. Special attention is paid to achieve robustness to many real world difficulties, including degenerate motions and unobservablity. A variety of helpful techniques are used, including: a relative manifold representation, a minimal-state inverse depth parameterization, and robust non-metric initialization and tracking. Importantly, to enable real-time operation and robustness, a novel numerical dog-leg solver is presented that employs multi-threaded, asynchronous, adaptive conditioning. In this approach, the conditioning edges of the SLAM graph are adaptively identified and solved for both synchronously and asynchronously. In this way one thread focuses on a small number of temporally immediate parameters and hence constitute a natural “front-end”; the other thread adaptively focuses on larger portions of the SLAM problem, and hence is able to re-estimate past parameters in the presence of new information: an ability that is useful for self-calibration, during degenerate motions, or when bias and the direction of gravity are poorly observed. Experiments with real and simulated data for both indoor and outdoor scenarios demonstrate that asynchronous adaptive conditioning is accurate, and able to closely track the batch SLAM maximum likelihood solution in real time. [ABSTRACT FROM AUTHOR]

The use of multiple sensors for ego-motion estimation is an approach often used to provide more accurate and robust results. However, when representing ego-motion as a discrete series of poses, fusing information of unsynchronized sensors is not straightforward. The framework described in this paper aims to provide a unified solution for solving ego-motion estimation problems involving high-rate unsynchronized devices. Instead of a discrete-time pose representation, we present a continuous-time formulation that makes use of cumulative cubic B-Splines parameterized in the Lie Algebra of the group $$\mathbb {SE}3$$ . This trajectory representation has several advantages for sensor fusion: (1) it has local control, which enables sliding window implementations; (2) it is $$C^2$$ continuous, allowing predictions of inertial measurements; (3) it closely matches torque-minimal motions; (4) it has no singularities when representing rotations; (5) it easily handles measurements from multiple sensors arriving a different times when timestamps are available; and (6) it deals with rolling shutter cameras naturally. We apply this continuous-time framework to visual-inertial simultaneous localization and mapping and show that it can also be used to calibrate the entire system. [ABSTRACT FROM AUTHOR]

Large scale exploration of the environment requires a constant time estimation engine. Bundle adjustment or pose relaxation do not fulfil these requirements as the number of parameters to solve grows with the size of the environment. We describe a relative simultaneous localisation and mapping system (RSLAM) for the constant-time estimation of structure and motion using a binocular stereo camera system as the sole sensor. Achieving robustness in the presence of difficult and changing lighting conditions and rapid motion requires careful engineering of the visual processing, and we describe a number of innovations which we show lead to high accuracy and robustness. In order to achieve real-time performance without placing severe limits on the size of the map that can be built, we use a topo-metric representation in terms of a sequence of relative locations. When combined with fast and reliable loop-closing, we mitigate the drift to obtain highly accurate global position estimates without any global minimisation. We discuss some of the issues that arise from using a relative representation, and evaluate our system on long sequences processed at a constant 30-45 Hz, obtaining precisions down to a few meters over distances of a few kilometres. [ABSTRACT FROM AUTHOR]

We are concerned with improving the range resolution of stereo vision for entry, descent, and landing (EDL) missions to Mars and other planetary bodies. The goal is to create accurate and precise three-dimensional planetary surface-structure estimates by filtering sequences of stereo images taken from an autonomous landing vehicle. We describe a sliding window filter (SWF) approach based on delayed state marginalization. The SWF can run in constant time, yet still achieve experimental results close to those of the bundle adjustment solution. This technique can scale from the offline batch least-squares solution to fast online incremental solutions. For instance, if the window encompasses all time, the solution is equivalent to full bundle adjustment; if only one time step is maintained, the solution matches the extended Kalman filter; if poses and landmarks are slowly marginalized out over time such that the state vector ceases to grow, then the filter becomes constant time, like visual odometry. Within the constant time regime, the sliding window approach demonstrates convergence properties that are close to those of the full batch solution and strictly superior to visual odometry. Experiments with real data show that ground structure estimates follow the expected convergence pattern that is predicted by theory. These experiments indicate the effectiveness of filtering long-range stereo for EDL. © 2010 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

In this paper we describe a relative approach to simultaneous localization and mapping, based on the insight that a continuous relative representation can make the problem tractable at large scales. First, it is well known that bundle adjustment is the optimal non-linear least-squares formulation for this problem, in that its maximum-likelihood form matches the definition of the Cramer—Rao lower bound. Unfortunately, computing the maximum-likelihood solution is often prohibitively expensive: this is especially true during loop closures, which often necessitate adjusting all parameters in a loop. In this paper we note that it is precisely the choice of a single privileged coordinate frame that makes bundle adjustment costly, and that this expense can be avoided by adopting a completely relative approach. We derive a new relative bundle adjustment which, instead of optimizing in a single Euclidean space, works in a metric space defined by a manifold. Using an adaptive optimization strategy, we show experimentally that it is possible to solve for the full maximum-likelihood solution incrementally in constant time, even at loop closure. Our approach is, by definition, everywhere locally Euclidean, and we show that the local Euclidean estimate matches that of traditional bundle adjustment. Our system operates online in realtime using stereo data, with fast appearance-based loop closure detection. We show results on over 850,000 images that indicate the accuracy and scalability of the approach, and process over 330 GB of image data into a relative map covering 142 km of Southern England. To demonstrate a baseline sufficiency for navigation, we show that it is possible to find shortest paths in the relative maps we build, in terms of both time and distance. Query images from the web of popular landmarks around London, such as the London Eye or Trafalgar Square, are matched to the relative map to provide route planning goals. [ABSTRACT FROM AUTHOR]

In this paper we describe a body of work aimed at extending the reach of mobile navigation and mapping. We describe how running topological and metric mapping and pose estimation processes concurrently, using vision and laser ranging, has produced a full six-degree- of-freedom outdoor navigation system. It is capable of producing intricate three-dimensional maps over many kilometers and in real time. We consider issues concerning the intrinsic quality of the built maps and describe our progress towards adding semantic labels to maps via scene de-construction and labeling. We show how our choices of representation, inference methods and use of both topological and metric techniques naturally allow us to fuse maps built from multiple sessions with no need for manual frame alignment or data association. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editors discuss various research papers related to autonomous robots within the issue on topics including simultaneous localization and mapping (SLAM), introspection and adaptive behavior in robots, and damage recovery in robotics.

The article summarizes the results of the International Conference on Robotics and Automation (ICRA) 2011 Workshop on Long-Term Autonomy and the Robotics Science and Systems (RSS) 2011 Workshop on Autonomous Long-Term Operation in Novel Environments (ALONE). The workshops were attended by prominent researchers to establish the key issues involved in building robotics that operate in large-scale environments. Examples of the successes in the field are cited including the Mars Exploration Rovers.