The New College Vision and Laser Data Set

Smith, Mike; Baldwin, Ian; Churchill, Winston; Paul, Rohan; Newman, Paul

doi:10.1177/0278364909103911

Cited by 272 publications

(217 citation statements)

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“…Exemplars were obtained using the New College data set [26]. Data were gathered from a two wheeled Segway RMP~200 platform with two vertically aligned SICK LMS291-S14 laser sensors mounted on the side of the vehicle.…”

Section: Resultsmentioning

confidence: 99%

Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data

Smith

Posner

Newman

2010

Robotics: Science and Systems VI

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Abstract-This paper concerns the creation of efficient surface representations from laser point clouds. We produce a continuous, implicit, non-parametric representation with an update time that is constant. The algorithm places no restriction on the complexity of the underlying workspace surfaces and automatically prunes redundant data via an information theoretic criterion. This criterion makes the use of Gaussian Process regression a natural choice. We adopt a formulation which handles the typical non-functional relation between XY-location and elevation allowing us to map arbitrary environments. Results are presented that use real and synthetic data to analyse the trade-off between compression level and reconstruction error. We attain decimation factors in excess of two orders of magnitude without significant degradation in fidelity.

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Section: Resultsmentioning

confidence: 99%

Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data

Smith

Posner

Newman

2010

Robotics: Science and Systems VI

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“…We use the popular Intel dataset (∼420m) collected at the Intel Research Laboratory (Seattle, WA), which is available with the downloaded version of g2o. Also, we use the New College dataset (∼2.2km), an outdoors environment from [12], and a multisession dataset (∼4km) in an indoor environment (Bicocca campus) from the RAWSEEDS project [13]. Our method can use any graph optimizer (e.g.…”

Section: Methodsmentioning

confidence: 99%

Realizing, reversing, recovering: Incremental robust loop closing over time using the iRRR algorithm

Latif

Cadena

Neira

2012

2012 IEEE/RSJ International Conference on Intelligent Robots and Systems

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Abstract-The ability to detect failures and reconsider information over time is crucial for long term robust autonomous robot applications. This applies to loop closure decisions in localization and mapping systems. This paper describes a method to analyze all available information up to date in order to robustly remove past incorrect loop closures from the optimization process. The main novelties of our algorithm are: 1. incrementally reconsidering loop closures and 2. handling multi-session, spatially related or unrelated experiments. We validate our proposal in real multi-session experiments showing better results than those obtained by state of the art methods.

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“…It consists of 81 dense 3D scans covering an area of 292 m × 167 m along a trajectory of 723 m. This dataset contains a total of 20 million end points. In a further experiment, we used laser range data of the New College data set (Smith et al, 2009) (Epoch C, 14 million end points in total). This data was recorded in a large-scale outdoor environment with two fixed laser scanners sweeping to the left and right side of the robot as it advances.…”

Section: D Models From Real Sensor Datamentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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The New College Vision and Laser Data Set

Cited by 272 publications

References 0 publications

Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data

Efficient Non-Parametric Surface Representations Using Active Sampling for Push Broom Laser Data

Realizing, reversing, recovering: Incremental robust loop closing over time using the iRRR algorithm

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

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