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

Abstract

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.

LiDAR-Based Glass Detection for Improved Occupancy Grid Mapping

“…Singh et al [ 18 ] proposed a Bayesian filter approach to fusion a laser scanner and sonar to reduce mapping uncertainty caused by transparent objects. This approach was affected by the short-range measurement reading from the sonar sensor.…”

Removal of sonar wave interference in multi-robot system for the efficient SLAM by randomized triggering time technique

“…Occupancy grid mapping is proposed in late 1980’s and it is still implemented because of feature such as simplicity, easy to implemented and low computation is required (Elfes, 1989a, 1989b; Singh and Nagla, 2019e). Sonar occupancy grid mapping is generated by various techniques such as Grid Map, EKF, ICP, feature based mapping using constant depth of sonar, grid updating model to reduce the effect of specular reflection, sensor fusion in grid mapping and dedicated filter (Lim and Cho, 1994; Leonard and Durrant-Whyte, 2012; Nagla et al , 2012, 2015; Patle et al , 2016; Singh and Nagla, 2019b, 2019d). However, the reliability of the generated occupancy grid mapping is low because of spatial discretization, cross talk, wave interference, imprecision in determining the exact value of the probability in the particular cell, heavy computational load, lack of metric information, etc.…”

“…(Siciliano and Khatib, 2016). However, sonar sensor had few limitations such as cross talk, poor angular resolution, wave interferences and specular reflection (Elfes, 1989a; Murphy, 2000; Thrun, 2003; Siciliano and Khatib, 2016; Singh and Nagla, 2019d, 2019e, 2019f). Because of the cross talk and the specular reflection in sonar sensors, the range information of the sonar sensor becomes altered (due to wave interferences) compare to actual range.…”

Comparative analysis of range sensors for the robust autonomous navigation – a review

“…The range sensors fitted on mobile robot for performing different tasks in outdoor environment get serious failure in the working due the effect of Multi Data Sensor Fusion, Dedicated Filters, Modified Probabilistic approaches, etc. Nagla et al (2012 and, Santos et al (2015), Huang et al (2018), Singh and Nagla (2019), Wei et al (2018aWei et al ( , 2018b…”