Selective attention of primates or human being has been modeled and implemented by many vision researchers in a decade. And also several attempts, which improve object recognition using selective attention model, have existed. But there are few researches in the gaze planning for the results of selective attention process. Therefore we propose a planning method based on edge information in the attended regions. And we explain the edge description methods (edge density and entropy) and also propose a new description method, edge uniformity. We evaluate the performance of the methods in the viewpoint of attentive object recognition.

Rao-Blackwellized particle filter (RBPF) has been to the fore as one of methods to solve simultaneous localization and mapping (SLAM) problem, i.e. RBPF-SLAM. The RBPF-SLAM, however, has been suffering from the particle depletion problem and the convergence problem caused by the improper posterior density and brutal rejection and replication of particles during resampling. We present a new technique to overcome those problems in RBPF-SLAM by keeping the particle diversity using particle formation maintenance (PFM). The triangular mesh structure is adaptively generated as a one form of PFM and it completely replaces the resampling part in RBPF-SLAM. Its considerable improvements regarding robot pose and features were shown in simulation by comparing conventional methods, i.e. FastSLAM 2.0 and PSO based FastSLAM.

This paper presents a real-time dynamic obstacle detection algorithm using a scan matching method considering image information from a mobile robot equipped with a camera and a laser scanner. By combining image and laser scan data, we extract a scan segment corresponding to the dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments, the extracted scan segment is temporarily removed. After obtaining a good robot position, the position of the dynamic obstacle is calculated based on the robot’s position. Through two experimental scenarios, the performance of the proposed algorithm is demonstrated.

Expansive-Spaces Trees (EST) is a single-query sampling-based path planner. When EST is applied to robot navigation in dynamic environments, EST confronts the risk of a collision with dynamic obstacles since it has generated a path without any consideration for dynamic obstacles. This paper proposes an efficient path replanning technique for EST-based robot navigation in dynamic environments. The proposed technique replans a collision-free and efficient path instead of the original EST path which may cause a collision with dynamic obstacles. Besides, the replanned path can be easily merged into the original EST path because it preserves the property of EST. Simulation results in various dynamic environments reveal that the proposed technique can successfully replan a collision-free and efficient path for EST-based navigation.