A novel mobile robots 3D-perception obstacle regions method in indoor environment based on Improved Salient Region Extraction (ISRE) is proposed. This model acquires the original image by the Kinect sensor and then gains Original Salience Map (OSM) and Intensity Feature Map (IFM) from the original image by the salience filtering algorithm. The IFM was used as the input neutron of PCNN. In order to make the ignition range more exact, PCNN ignition pulse input was further improved as follows: point multiplication algorithm was taken between PCNN internal neuron and binarization salience image of OSM; then we determined the final ignition pulse input. The salience binarization region abstraction was fulfilled by improved PCNN multiple iterations finally. Finally, the binarization area was mapped to the depth map obtained by Kinect sensor, and mobile robot can achieve the obstacle localization function. The method was conducted on a mobile robot (Pioneer3-DX). The experimental results demonstrated the feasibility and effectiveness of the proposed algorithm.

This paper proposes a distributed intelligent assistant robotic system in order to improve the quality of the elderly people's life in the population aging society. The system is composed of embedded distributed sensor networks and multirobot intelligent platform. In the proposed system, we use SP 2 ATM (simultaneous path planning and topological mapping) with RBPF (Rao-Blackwellized particle filter) for path planning and localization of mobile robots. In order to perform service tasks, an accurate and reliable 3D environment map is reconstructed by using an effective 3D reconstruction technique from multiview stereo. Besides, the system provides user human tracking services based on multifeature mean shift under the double-layer locating mechanism. To improve the feasibility and reliability of the system, this proposed system is developed based on distributed control technology RTM (robot technology middleware). This paper presents the architecture of the proposed system and the experimental results verify effectiveness of the approaches.

Visual object tracking is a fundamental research topic in computer vision. In this paper, we proposed a novel hybrid tracking method based on Pulse Coupled Neural Network (PCNN) and Multiple Instance Learning (MIL). Most modern trackers may be inaccurate when the training samples are imprecise which causes drift. To resolve these problems, MIL method is introduced into the tracking task, which can alleviate drift to some extent. However, the MIL tracker may detect the positive sample that is less important. PCNN is different from traditional artificial neural networks, which can be applied in many image processing fields, such as image segmentation. So, the PCNN was employed as sample detector which can know the most important sample when training the classifier. Then, a more robust and much faster tracker is proposed to approximately maximize the bag likelihood function. Empirical results on a large set of sequences demonstrate the superior performance of the proposed approach in robustness, stability and efficiency to state-of-the-art methods in the literature.