Feature-based detection using Bayesian data fusion

Ayodeji, Akiwowo; Eftekhari, Mohammad

doi:10.1080/19479832.2013.824029

Cited by 10 publications

(6 citation statements)

References 9 publications

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“…Similar conclusions could be made if the SBF method is compared with the eight filtering algorithms presented by Sithole and Vosselman [17]. The future work will focus on the improvement of the proposed filter to reduce the type II errors, markov random field model [48] is introduced to analyze the spatial topology of the segments, multi-source data fusion [49] is employed and parallel computing is performed to promote the efficiency.…”

Section: Discussionmentioning

confidence: 77%

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Lin

Zhang

2014

Remote Sensing

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Abstract:Filtering is one of the core post-processing steps for Airborne Laser Scanning (ALS) point clouds. A segmentation-based filtering (SBF) method is proposed herein. This method is composed of three key steps: point cloud segmentation, multiple echoes analysis, and iterative judgment. Moreover, the third step is our main contribution. Particularly, the iterative judgment is based on the framework of the classic progressive TIN (triangular irregular network) densification (PTD) method, but with basic processing unit being a segment rather than a single point. Seven benchmark datasets provided by ISPRS Working Group III/3 are utilized to test the SBF algorithm and the classic PTD method. Experimental results suggest that, compared with the PTD method, the SBF approach is capable of preserving discontinuities of landscapes and removing the lower parts of large objects attached on the ground surface. As a result, the SBF approach is able to reduce omission errors and total errors by 18.26% and 11.47% respectively, which would significantly decrease the cost of manual operation required in post-processing.

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Section: Discussionmentioning

confidence: 77%

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Lin

Zhang

2014

Remote Sensing

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“…The future work will focus on the improvement of the proposed filter. For example, the markov random field model [54] might be introduced to analyze the spatial topology, and multisource data fusion [55] is employed to increase the accuracy.…”

Section: Discussionmentioning

confidence: 99%

Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

2014

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As one of the key steps in the processing of airborne light detection and ranging (LiDAR) data, filtering often consumes a huge amount of time and physical memory. Conventional sequential algorithms are often inefficient in filtering massive point clouds, due to their huge computational cost and Input/Output (I/O) bottlenecks. The progressive TIN (Triangulated Irregular Network) densification (PTD) filter is a commonly employed iterative method that mainly consists of the TIN generation and the judging functions. However, better quality from the progressive process comes at the cost of increasing computing time. Fortunately, it is possible to take advantage of state-of-the-art multi-core computing facilities to speed up this computationally intensive task. A streaming framework for filtering point clouds by encapsulating the PTD filter into independent computing units is proposed in this paper. Through overlapping multiple computing units and the I/O events, the efficiency of the proposed method is improved greatly. More importantly, this framework is adaptive to many filters. Experiments suggest that the proposed streaming PTD (SPTD) is able to improve the performance of massive point clouds processing and alleviate the I/O bottlenecks. The experiments also demonstrate that this SPTD allows the quick processing of massive point clouds with better adaptability. In a 12-core environment, the SPTD gains a speedup of 7.0 for filtering 249 million points.

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“…The proposed LAFs, coupled with an air blower and laser scanner, are used for reducing the effect of rain, humidity and dirt on laser scanner, whereas specular reflection/crosstalk in sonar (sonar ring) is reduced by SRSF (along with modified sonar ring) with a randomized sonar triggering time (RSTT) technique. This proposed technique is implemented on laser scanner and sonar ring fitted on the mobile robot, which is manually navigated in the harsh environment for the generation of efficient perception along with recursive Bayesian updating approach (Akiwowo and Eftekhari, 2013;Singh and Nagla, 2018a). A comparative analysis with conventional approaches is also given to validate the results obtained with respect to the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

A modified sensor fusion framework for quantifying and removing the effect of harsh environmental condition for reliable mobile robot mapping

Singh

Nagla

2019

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Purpose An autonomous mobile robot requires efficient perception of the environment to perform various tasks in a challenging environment. The precise sensory information from the range sensors is required to accomplish prerequisites, such as SLAM, path planning and localization. But the accuracy and precision of the sensors become unreliable in harsh environmental conditions because of the effect of rain, dust, humidity, fog and smoke. The purpose of this paper is to generate robust mapping of the environment in harsh environmental conditions. Design/methodology/approach This paper presents a novel technique, averaging data with short range selection (ADWSRS), to reduce the effect of harsh environmental (rain, wind, humidity, etc.) conditions on sensory information (range) to generate reliable grid mapping. The sensory information on laser and sonar sensors in terms of probability values (occupied/unoccupied cell) in generating grid maps are fused after passing through two newly designed pre-processing filters: laser averaging filter and short range selection filter. This proposed approach relies on various aspects such as averaging laser data analogous to current pose of the sensor, selection of short range with respect to threshold value to remove the effect of specular reflection/crosstalk of sonar and a newly designed apparatus in which dirt cover (glass cover) and air blower are coupled to remove the influence of dirt, rain and humidity. Findings This proposed approach is tested in different environmental conditions, and to verify the consistency of the proposed approach, qualitative and quantitative analyses are carried out, which shows 42.5 per cent improvement in the probability value of occupied cells in the generated grid map. Originality/value The proposed ADWSRS approach reduced the effect of harsh environmental conditions such as fog, rain and smoke to generate efficient mapping of the environment, which may be implemented in diverse applications such as autonomous navigation, localization, path planning and mapping.

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Feature-based detection using Bayesian data fusion

Cited by 10 publications

References 9 publications

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Segmentation-Based Filtering of Airborne LiDAR Point Clouds by Progressive Densification of Terrain Segments

Streaming Progressive TIN Densification Filter for Airborne LiDAR Point Clouds Using Multi-Core Architectures

A modified sensor fusion framework for quantifying and removing the effect of harsh environmental condition for reliable mobile robot mapping

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