A Novel Filter for Terrain Mapping With Laser Rangefinders

Ye, Cang; Borenstein, J.

doi:10.1109/tro.2004.829457

Cited by 38 publications

(38 citation statements)

References 21 publications

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“…The data transfer rate is fixed at 57.6 kbps. The sensor is small, 75 x 70 x 60 mm, and weighs only 500 g. This sensor is intended for indoor use since sunlight may cause erroneous measurements [9] (as is the case with the LMS 200 Sick Laser Scanner [10]). Table I summarizes the manufacturer's specifications of the PBS-03JN sensor [6].…”

Section: Characteristics Of the Infrared Sensor Pbs-03jnmentioning

confidence: 99%

Characterization of Infrared Range-Finder PBS-03JN for 2-D Mapping

Alwan

Wagner²,

Wasson

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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-This paper presents a characterization study of the HOKUYO PBS-03JN Infrared range-finder and compares it to the characterization of the SICK LMS-200 laser rangefinder for use in indoor 2-D mapping. Many parameters that could affect the performance of the sensor including warm-up time, divergence of the detection beam, usable detection range in the azimuth, target surface, color, and size properties, incidence angle at the target, and the mixed pixels problem have been studied. This characterization, quantification of errors, and 3-D confidence in the distance readings of the sensor is vital for practical applications. These characteristics are compared to the counterpart characteristics of the laser range-finder. The PBS-03JN is a cost effective alternative to laser range-finders in indoor environments. The sensor is attractive due to lower power consumption, and its lightweight.

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Section: Characteristics Of the Infrared Sensor Pbs-03jnmentioning

confidence: 99%

Characterization of Infrared Range-Finder PBS-03JN for 2-D Mapping

Alwan

Wagner²,

Wasson

et al.

Proceedings of the 2005 IEEE International Conference on Robotics and Automation

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“…Thus, we compare the overall gain by computation of the difference between the map obtained in the experiment considered and the ground truth map. The influence of the method on the quality of the map is measured using a modified version of the performance index (PI) (Ye and Borenstein, 2004):…”

Section: 1mentioning

confidence: 99%

“…Some of the grid-based algorithms for rough terrain mapping use ad hoc formulations for mapping; e.g., Ye and Borenstein (2004) apply heuristics when they fuse consecutive laser range measurements into an elevation grid: their certainty measure is a simple hit/miss counter. On the other hand, there are 2.5D mapping methods which are based on probabilistic foundations.…”

mentioning

confidence: 99%

RGB–D terrain perception and dense mapping for legged robots

Belter

Łabęcki

Fankhauser

et al. 2016

International Journal of Applied Mathematics and Computer Science

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This paper addresses the issues of unstructured terrain modeling for the purpose of navigation with legged robots. We present an improved elevation grid concept adopted to the specific requirements of a small legged robot with limited perceptual capabilities. We propose an extension of the elevation grid update mechanism by incorporating a formal treatment of the spatial uncertainty. Moreover, this paper presents uncertainty models for a structured light RGB-D sensor and a stereo vision camera used to produce a dense depth map. The model for the uncertainty of the stereo vision camera is based on uncertainty propagation from calibration, through undistortion and rectification algorithms, allowing calculation of the uncertainty of measured 3D point coordinates. The proposed uncertainty models were used for the construction of a terrain elevation map using the Videre Design STOC stereo vision camera and Kinect-like range sensors. We provide experimental verification of the proposed mapping method, and a comparison with another recently published terrain mapping method for walking robots.

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“…Elevation map is widely used for outdoor unstructured terrain environment modeling problem [8][9][10][11][12]. This method uses 2-dimensional grid to represent plane location, and for each grid an elevation value is stored to represent the terrain height information, thus it is also referred as 2.5-dimension map.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic terrain modeling of mobile robots for outdoor applications using a scanning laser

Zhou

Qian

et al. 2012

2012 IEEE International Conference on Mechatronics and Automation

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In this paper the real-time three-dimensional terrain estimation and modeling problem of mobile robots for outdoor applications is considered. A uniform probabilistic sensor model for scanning lasers is created to transform measurements into elevation estimation in the form of normal distributions by multi-level coordination transformation, with uncertainties from un-modeled errors or sensors accuracies are treated as Gaussian noises. Under the elevation map description framework of the terrain model, the obtained elevation estimations are assigned to terrain cells according to their confidence boundary, and can be fused in a probabilistic manner to update the elevation map. Moreover, the terrain occlusion are considered and solved by local window detection method to improve the accuracy of the terrain model. Outdoor experimental results of a tracked robot with a scanning laser are given to demonstrate the effectiveness of the proposed method.

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A Novel Filter for Terrain Mapping With Laser Rangefinders

Cited by 38 publications

References 21 publications

Characterization of Infrared Range-Finder PBS-03JN for 2-D Mapping

Characterization of Infrared Range-Finder PBS-03JN for 2-D Mapping

RGB–D terrain perception and dense mapping for legged robots

Probabilistic terrain modeling of mobile robots for outdoor applications using a scanning laser

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