Hiroaki Masuzawa

Observation planning for efficient environment information summarization

Mapping is an activity of making a useful description of an environment. Not only geometric information such as free space but also object placements are important if the map is used for human-robot communication. We call such a map making environment information summarization because how to summarize may change depending on the purpose of the map. Environment information summarization usually includes searching for specified objects in the environment. It is, therefore, crucial to make a good observation plan for efficient summarization. We develop an observation planning method which uses object appearance models for appropriately handling a trade-off between visual data quality and vision cost. Experimental results using a vision-based humanoid robot show the effectiveness of the proposed planning method.

Observation planning for environment information summarization with deadlines

Mapping is an activity of making a useful description of an environment. Not only geometric information such as free space shape but also semantic information such as object names are sometimes important. We call such a map making environment information summarization because how to summarize may change depending on the purpose of the map and the context. One important aspect of such summarization is the deadline, which imposes a time constraint on the robots mapping activity. We therefore develop an observation planning method for the summarization with deadlines. The method can cope with various types of deadlines specified in the form of loss function. Experimental results shows various robot behaviors are generated by only changing the deadline specification.

A hierarchical SLAM for uncertain range data

This paper describes a new approach to SLAM problems using low quality range data. Vision sensors are useful for acquiring various kinds of environmental information but range data obtained by stereo vision is less reliable than other active sensors like laser range finders. False stereo matches often result in spurious obstacles, which may degrade the map when directly used in existing SLAM methods. We therefore propose a hierarchical approach in which local probabilistic occupancy maps are first generated to filter out such spurious obstacles and then used as inputs to an RBPF-based SLAM. Experimental results in simulation and in a real environment show that a consistent map can be generated by the proposed method with low quality stereo range data.