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FastSLAM is a popular method to solve the problem of simultaneous localization and mapping (SLAM). However, when the number of landmarks present in real environments increases, there are excessive comparisons of the measurement with all the existing landmarks in each particle. As a result, the execution speed will be too slow to achieve the objective of real-time navigation. Thus, this paper aims to improve the computational efficiency and estimation accuracy of conventional SLAM algorithms.

As an attempt to solve this problem, this paper presents a computationally efficient SLAM (CESLAM) algorithm, where odometer information is considered for updating the robot’s pose in particles. When a measurement has a maximum likelihood with the known landmark in the particle, the particle state is updated before updating the landmark estimates.

Simulation results show that the proposed CESLAM can overcome the problem of heavy computational burden while improving the accuracy of localization and mapping building. To practically evaluate the performance of the proposed method, a Pioneer 3-DX robot with a Kinect sensor is used to develop an RGB-D-based computationally efficient visual SLAM (CEVSLAM) based on Speeded-Up Robust Features (SURF). Experimental results confirm that the proposed CEVSLAM system is capable of successfully estimating the robot pose and building the map with satisfactory accuracy.

The proposed CESLAM algorithm overcomes the problem of the time-consuming process because of unnecessary comparisons in existing FastSLAM algorithms. Simulations show that accuracy of robot pose and landmark estimation is greatly improved by the CESLAM. Combining CESLAM and SURF, the authors establish a CEVSLAM to significantly improve the estimation accuracy and computational efficiency. Practical experiments by using a Kinect visual sensor show that the variance and average error by using the proposed CEVSLAM are smaller than those by using the other visual SLAM algorithms.

This study employed the commitment–trust theory in social psychology and relationship marketing to explore female customers' perception of channel integration quality in omnichannel retailing and its influence on their relationship commitment to and trust in the relationship with retailers, and thus on their stickiness. Channel integration quality consists of two dimensions: channel service configuration (channel choice breadth and channel service transparency) and integrated interactions (content consistency, process consistency and perceived fluency).

The study was carried out via a questionnaire survey, to which 868 valid responses were collected. The partial least squares technique was used to test the hypotheses.

Channel service transparency and perceived fluency influence relationship commitment; content consistency, process consistency and perceived fluency all have significant effects on trust. Interestingly, although less influential than integrated interactions, channel service configuration is the foundation of channel integration quality, testifying to its significant role.

This study provides strong evidence on how channel integration quality affects customer stickiness. Moreover, this study replicates the finding of significant relationships among relationship commitment, trust and stickiness in omnichannel retailing.