Experiments in Visual Localisation around Underwater Structures

Nuske, Stephen; Roberts, Jonathan; Prasser, David; Wyeth, Gordon

doi:10.1007/978-3-642-13408-1_27

Cited by 6 publications

(3 citation statements)

References 10 publications

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“…This is similar to the approach in [12] which used gradients in the color image 2 By comparison, momentary failure of visual odometry as part of a Vision SLAM system can be result in significant error while here we use the depth information. When OpenGL renders an image, such as our 3-D model, it natively uses the Z-buffer to determine hidden surface removal.…”

Section: B Likelihood Functionmentioning

confidence: 92%

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Fallon

Johannsson

Leonard

2012

2012 IEEE International Conference on Robotics and Automation

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This paper presents Kinect Monte Carlo Localization (KMCL), a new method for localization in three dimensional indoor environments using RGB-D cameras, such as the Microsoft Kinect. The approach makes use of a low fidelity a priori 3-D model of the area of operation composed of large planar segments, such as walls and ceilings, which are assumed to remain static. Using this map as input, the KMCL algorithm employs feature-based visual odometry as the particle propagation mechanism and utilizes the 3-D map and the underlying sensor image formation model to efficiently simulate RGB-D camera views at the location of particle poses, using a graphical processing unit (GPU). The generated 3D views of the scene are then used to evaluate the likelihood of the particle poses. This GPU implementation provides a factor of ten speedup over a pure distance-based method, yet provides comparable accuracy. Experimental results are presented for five different configurations, including: (1) a robotic wheelchair, (2) a sensor mounted on a person, (3) an Ascending Technologies quadrotor, (4) a Willow Garage PR2, and (5) an RWI B21 wheeled mobile robot platform. The results demonstrate that the system can perform robust localization with 3D information for motions as fast as 1.5 meters per second. The approach is designed to be applicable not just for robotics but other applications such as wearable computing.

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Section: B Likelihood Functionmentioning

confidence: 92%

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Fallon

Johannsson

Leonard

2012

2012 IEEE International Conference on Robotics and Automation

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show abstract

“…This is similar to the approach in [81] which used gradients in the color image, however here we use the depth information. When OpenGL renders an image, such as our 3-D model, it natively uses the Z-buffer to determine hidden surface removal.…”

Section: Generated Range-image Ray-to-plane Functionmentioning

confidence: 99%

Toward lifelong visual localization and mapping

Johannsson¹

2013

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Mobile robotic systems operating over long durations require algorithms that are robust and scale efficiently over time as sensor information is continually collected. For mobile robots one of the fundamental problems is navigation; which requires the robot to have a map of its environment, so it can plan its path and execute it. Having the robot use its perception sensors to do simultaneous localization and mapping (SLAM) is beneficial for a fully autonomous system. Extending the time horizon of operations poses problems to current SLAM algorithms, both in terms of robustness and temporal scalability. To address this problem we propose a reduced pose graph model that significantly reduces the complexity of the full pose graph model. Additionally we develop a SLAM system using two different sensor modalities: imaging sonars for underwater navigation and vision based SLAM for terrestrial applications.Underwater navigation is one application domain that benefits from SLAM, where access to a global positioning system (GPS) is not possible. In this thesis we present SLAM systems for two underwater applications. First, we describe our implementation of real-time imaging-sonar aided navigation applied to in-situ autonomous ship hull inspection using the hovering autonomous underwater vehicle (HAUV). In addition we present an architecture that enables the fusion of information from both a sonar and a camera system. The system is evaluated using data collected during experiments on SS Curtiss and USCGC Seneca. Second, we develop a feature-based navigation system supporting multi-session mapping, and provide an algorithm for re-localizing the vehicle between missions. In addition we present a method for managing the complexity of the estimation problem as new information is received. The system is demonstrated using data collected with a REMUS vehicle equipped with 3 a BlueView forward-looking sonar.The model we use for mapping builds on the pose graph representation which has been shown to be an efficient and accurate approach to SLAM. One of the problems with the pose graph formulation is that the state space continuously grows as more information is acquired. To address this problem we propose the reduced pose graph (RPG) model which partitions the space to be mapped and uses the partitions to reduce the number of poses used for estimation. To evaluate our approach, we present results using an online binocular and RGB-Depth visual SLAM system that uses place recognition both for robustness and multi-session operation. Additionally, to enable large-scale indoor mapping, our system automatically detects elevator rides based on accelerometer data. We demonstrate long-term mapping using approximately nine hours of data collected in the MIT Stata Center over the course of six months. Ground truth, derived by aligning laser scans to existing floor plans, is used to evaluate the global accuracy of the system. Our results illustrate the capability of our visual SLAM system to map a large scale environment over an exte...

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“…Monte Carlo localization allowed the Minerva robotic museum guide to operate for 44 km over 2 weeks [71]. More recently it has been used by Nuske et al to localize an AUV relative to a marine structure using a camera [59], exploiting GPU-accelerated image formation to facilitate large particle sets.…”

Section: Simultaneous Localization 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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Experiments in Visual Localisation around Underwater Structures

Cited by 6 publications

References 10 publications

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Efficient scene simulation for robust monte carlo localization using an RGB-D camera

Toward lifelong visual localization and mapping

Simultaneous Localization and Mapping in Marine Environments

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