Glass confidence maps building based on neural networks using laser range-finders for mobile robots

Jiang, Jun; Miyagusuku, Renato; Yamashita, Atsushi; Asama, Hajime

doi:10.1109/sii.2017.8279246

Cited by 19 publications

(8 citation statements)

References 8 publications

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“…Then, these results were finally integrated with a mapping algorithm. Jiang et al [ 13 ] proposed a neural network algorithm that uses reflectivity, incident angle, and distance measurements to classify glass and non-glass objects using a laser range finder. Awais [ 14 ] and Foster [ 10 ] modelled the probability of receiving reflection back from the glass as a function of distance and an angle.…”

Section: Related Workmentioning

confidence: 99%

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Tibebu

Roche

Silva

et al. 2021

Sensors

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Creating an accurate awareness of the environment using laser scanners is a major challenge in robotics and auto industries. LiDAR (light detection and ranging) is a powerful laser scanner that provides a detailed map of the environment. However, efficient and accurate mapping of the environment is yet to be obtained, as most modern environments contain glass, which is invisible to LiDAR. In this paper, a method to effectively detect and localise glass using LiDAR sensors is proposed. This new approach is based on the variation of range measurements between neighbouring point clouds, using a two-step filter. The first filter examines the change in the standard deviation of neighbouring clouds. The second filter uses a change in distance and intensity between neighbouring pules to refine the results from the first filter and estimate the glass profile width before updating the cartesian coordinate and range measurement by the instrument. Test results demonstrate the detection and localisation of glass and the elimination of errors caused by glass in occupancy grid maps. This novel method detects frameless glass from a long range and does not depend on intensity peak with an accuracy of 96.2%.

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Section: Related Workmentioning

confidence: 99%

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Tibebu

Roche

Silva

et al. 2021

Sensors

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“…(Murphy, 2000;Jetto et al, 1999;Nagla et al, 2011Nagla et al, , 2015Singh and Nagla, 2018b;Stepan et al, 2005). The intensity-based localization of glass was also introduced in the past by Jiang et al (2017); the stated approach works on the principle of intensity protocol. But still the consistent and reliable perception of the complex environment has to be achieved because stated techniques rely on the probabilistic approaches (sonar sensor model) to convert the range information into the probabilistic models.…”

Section: Related Workmentioning

confidence: 99%

Multi-data sensor fusion framework to detect transparent object for the efficient mobile robot mapping

Singh

Nagla

2019

IJIUS

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Purpose An efficient perception of the complex environment is the foremost requirement in mobile robotics. At present, the utilization of glass as a glass wall and automated transparent door in the modern building has become a highlight feature for interior decoration, which has resulted in the wrong perception of the environment by various range sensors. The perception generated by multi-data sensor fusion (MDSF) of sonar and laser is fairly consistent to detect glass but is still affected by the issues such as sensor inaccuracies, sensor reliability, scan mismatching due to glass, sensor model, probabilistic approaches for sensor fusion, sensor registration, etc. The paper aims to discuss these issues. Design/methodology/approach This paper presents a modified framework – Advanced Laser and Sonar Framework (ALSF) – to fuse the sensory information of a laser scanner and sonar to reduce the uncertainty caused by glass in an environment by selecting the optimal range information corresponding to a selected threshold value. In the proposed approach, the conventional sonar sensor model is also modified to reduce the wrong perception in sonar as an outcome of the diverse range measurement. The laser scan matching algorithm is also modified by taking out the small cluster of laser point (w.r.t. range information) to get efficient perception. Findings The probability of the occupied cells w.r.t. the modified sonar sensor model becomes consistent corresponding to diverse sonar range measurement. The scan matching technique is also modified to reduce the uncertainty caused by glass and high computational load for the efficient and fast pose estimation of the laser sensor/mobile robot to generate robust mapping. These stated modifications are linked with the proposed ALSF technique to reduce the uncertainty caused by glass, inconsistent probabilities and high load computation during the generation of occupancy grid mapping with MDSF. Various real-world experiments are performed with the implementation of the proposed approach on a mobile robot fitted with laser and sonar, and the obtained results are qualitatively and quantitatively compared with conventional approaches. Originality/value The proposed ASIF approach generates efficient perception of the complex environment contains glass and can be implemented for various robotics applications.

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“…The fact that glass is a partially transparent and partially reflective surface provides valuable information since one can actually observe two distinct signals from the transmitted and reflected parts of the scene. This is widely used in multi-signal time of flight sensors [Foster et al 2013;Jiang et al 2017;Koch et al 2017b,a]. Several passive imaging approaches also rely on detecting and disentangling these two distinct image components.…”

Section: Detecting Planar Reflective Surfacesmentioning

confidence: 99%

“…They extend the popular occupancy grid mapping, giving it the ability to include surfaces that are only observable in a fraction of the frames in which they should have been seen. Jiang et al [2017] use the intensity of the return signal for a time-of-flight (TOF) sensor, the distance to the surface, and the incident angle as features for a neural network-based classification of glass surfaces in indoor environments. While sufficient for robot navigation, their approach cannot yield high accuracy scan data.…”

Section: Scenes With Mirror and Glass Surfacesmentioning

confidence: 99%

Reconstructing scenes with mirror and glass surfaces

et al. 2018

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Fig. 1. Reconstructing a scene with mirrors. From left to right: Input color image showing the scanner with attached AprilTag in a mirror, reconstructed geometry without taking the mirrors into account, reconstruction taking the detected mirrors (rendered as cross-hatched area) into account and a photorealistic rendering of the scene including the mirrors. Detecting the mirrors is crucial for accurate geometry reconstruction and realistic rendering.Planar reflective surfaces such as glass and mirrors are notoriously hard to reconstruct for most current 3D scanning techniques. When treated naïvely, they introduce duplicate scene structures, effectively destroying the reconstruction altogether. Our key insight is that an easy to identify structure attached to the scanner-in our case an AprilTag-can yield reliable information about the existence and the geometry of glass and mirror surfaces in a scene. We introduce a fully automatic pipeline that allows us to reconstruct the geometry and extent of planar glass and mirror surfaces while being able to distinguish between the two. Furthermore, our system can automatically segment observations of multiple reflective surfaces in a scene based on their estimated planes and locations. In the proposed setup, minimal additional hardware is needed to create high-quality results. We demonstrate this using reconstructions of several scenes with a variety of real mirrors and glass.

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Glass confidence maps building based on neural networks using laser range-finders for mobile robots

Cited by 19 publications

References 8 publications

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

Multi-data sensor fusion framework to detect transparent object for the efficient mobile robot mapping

Reconstructing scenes with mirror and glass surfaces

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