Fast iterative alignment of pose graphs with poor initial estimates

Olson, Edwin; Leonard, John J.; Teller, Seth

doi:10.1109/robot.2006.1642040

Cited by 368 publications

(402 citation statements)

References 12 publications

(14 reference statements)

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“…One solution would be to optimise over relative constraints between poses using pose-graph optimisation [12] [23]. The relative constraints between two poses T i and T j can be calculated easily:…”

Section: B Loop Closure Correctionmentioning

confidence: 99%

Scale Drift-Aware Large Scale Monocular SLAM

Strasdat¹,

Montiel²,

Davison³

2010

Robotics: Science and Systems VI

354

309

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Abstract-State of the art visual SLAM systems have recently been presented which are capable of accurate, large-scale and real-time performance, but most of these require stereo vision. Important application areas in robotics and beyond open up if similar performance can be demonstrated using monocular vision, since a single camera will always be cheaper, more compact and easier to calibrate than a multi-camera rig.With high quality estimation, a single camera moving through a static scene of course effectively provides its own stereo geometry via frames distributed over time. However, a classic issue with monocular visual SLAM is that due to the purely projective nature of a single camera, motion estimates and map structure can only be recovered up to scale. Without the known inter-camera distance of a stereo rig to serve as an anchor, the scale of locally constructed map portions and the corresponding motion estimates is therefore liable to drift over time.In this paper we describe a new near real-time visual SLAM system which adopts the continuous keyframe optimisation approach of the best current stereo systems, but accounts for the additional challenges presented by monocular input. In particular, we present a new pose-graph optimisation technique which allows for the efficient correction of rotation, translation and scale drift at loop closures. Especially, we describe the Lie group of similarity transformations and its relation to the corresponding Lie algebra. We also present in detail the system's new image processing front-end which is able accurately to track hundreds of features per frame, and a filter-based approach for feature initialisation within keyframe-based SLAM. Our approach is proven via large-scale simulation and real-world experiments where a camera completes large looped trajectories.

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Section: B Loop Closure Correctionmentioning

confidence: 99%

Scale Drift-Aware Large Scale Monocular SLAM

Strasdat¹,

Montiel²,

Davison³

2010

Robotics: Science and Systems VI

354

309

View full text Add to dashboard Cite

show abstract

“…Many iterative solutions to the SLAM problem have been presented, such as stochastic gradient descent [28,60], relaxation [10], preconditioned conjugate gradient [43], and loopy belief propagation [63].…”

Section: Pose Graph Optimization Using Smoothing and Mappingmentioning

confidence: 99%

Simultaneous Localization and Mapping in Marine Environments

Fallon

Johannsson

Kaess

et al. 2012

Marine Robot Autonomy

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Accurate navigation is a fundamental requirement for robotic systemsmarine and terrestrial. For an intelligent autonomous system to interact effectively and safely with its environment, it needs to accurately perceive its surroundings. While traditional dead-reckoning filtering can achieve extremely low drift rates, the localization accuracy decays monotonically with distance traveled. Other approaches (such as external beacons) can help; nonetheless, the typical prerogative is to remain at a safe distance and to avoid engaging with the environment. In this chapter we discuss alternative approaches which utilize onboard sensors so that the robot can estimate the location of sensed objects and use these observations to improve its own navigation as well as its perception of the environment. This approach allows for meaningful interaction and autonomy. Three motivating autonomous underwater vehicle (AUV) applications are outlined herein. The first fuses external range sensing with relative sonar measurements. The second application localizes

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“…Other pure 6D SLAM backends such as the tree optimizer by Olson et al (Olson, Leonard, & Teller, 2006) are becomming available (Grisetti, Grzonka, Stachniss, Pfaff, & Burgard, 2007).…”

Section: Mapping Environments In Three Dimensionsmentioning

confidence: 99%

“…The SLAM backend uses fast matrix computations exploiting the sparse structure of the corresponding SLAM graphs (Davis, 2006). Other backends such as Olson's graph optimization, extended to six DoF (Kaess, Ranganathan, & Dellaert, 2007;Olson et al, 2006) or treemap by Frese (2007), might be used as well. Using different paths in Figure 3, the different mapping strategies are created.…”

Section: Mapping Strategiesmentioning

confidence: 99%

Benchmarking urban six‐degree‐of‐freedom simultaneous localization and mapping

Wulf

Nüchter

Hertzberg

et al. 2008

Journal of Field Robotics

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Quite a number of approaches for solving the simultaneous localization and mapping (SLAM) problem exist by now. Some of them have recently been extended to mapping environments with six-degree-of-freedom poses, yielding 6D SLAM approaches. To demonstrate the capabilities of the respective algorithms, it is common practice to present generated maps and successful loop closings in large outdoor environments. Unfortunately, it is nontrivial to compare different 6D SLAM approaches objectively, because ground truth data about the outdoor environments used for demonstration are typically unavailable. We present a novel benchmarking method for generating the ground truth data based on reference maps. The method is then demonstrated by comparing the absolute performance of some previously existing 6D SLAM algorithms that build a large urban outdoor map. C

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Fast iterative alignment of pose graphs with poor initial estimates

Cited by 368 publications

References 12 publications

Scale Drift-Aware Large Scale Monocular SLAM

Scale Drift-Aware Large Scale Monocular SLAM

Simultaneous Localization and Mapping in Marine Environments

Benchmarking urban six‐degree‐of‐freedom simultaneous localization and mapping

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