Masahiro Tomono

A grasp planning for picking up an unknown object for a mobile manipulator

This paper describes a grasp planning for a mobile manipulator which works in real environment. Mobile robot studies up to now that manipulate an object in real world practically use ID tag on an object or an object model which is given to the robot in advance. The authors aim to develop a mobile manipulator that can acquire an object model through video images and can manipulate the object. In this approach, the robot can manipulate an unknown object autonomously. A grasp planning proposed in this paper can find a stable grasp pose from the automatically generated model which contains redundant data and the shape error of the object. Experiments show the effectiveness of the proposed method

3-D Object Map Building Using Dense Object Models with SIFT-based Recognition Features

This paper presents a method of object map building using object models created from image sequences captured by a single camera. Object map is a highly structured map, which is built by placing 3-D object models on the floor plane according to object recognition results. To increase the efficiency of object map building, we propose a framework to integrate dense shape and recognition features into an object model. Experimental results show that an object map of a room was built successfully using the proposed object models

Mobile robot localization based on an inaccurate map

Proposes a method of global localization on a map with large uncertainty. Since the pose of a mobile robot represented in a global frame of reference might be inconsistent on an inaccurate map, the method represents the robot pose in a local frame attached to a landmark. When the robot finds a new landmark, the robot transforms its pose from the old landmark frame to the new one based on the relative pose between the landmarks. Map errors are represented by probability density functions defined on these relative poses. The paper presents an extension of Markov localization which is augmented so as to incorporate the map errors into data fusion process, and shows experimental results.

Object-based localization and mapping using loop constraints and geometric prior knowledge

This paper presents a method of building a structured map, which consists of objects such as furniture. We represent a map as a graph, in which a node represents an object and an arc represents a relative pose between objects. The robot localizes itself and builds a map using odometry readings and sensor data obtained by object recognition. To correct the map distortion caused by errors in the data, the robot utilizes loops as geometric constraints, and imports geometric knowledge provided by a hand-made map. Experiments show that the root successfully built a map of corridors, and a map of a room having many objects.

Mobile robot navigation in indoor environments using object and character recognition

Navigation in unknown environments requires the robot to obtain the destination positions without a map. The utilization of model-based object recognition would be a solution, where the robot can estimate the destination positions from geometric relationships between the recognized objects and the robot. This paper presents a robot system for this kind of navigation, in which the robot navigates itself to the room designated by room number. The robot has an environment model including a corridor and a door with a room number plate, and utilizes the model for the efficient recognition of the objects and the estimation of their positions.

3-D object modeling by a camera equipped on a mobile robot

This paper describes 3-D shape modeling performed by a mobile robot. We propose a method in which the robot in the real environment acquires the accurate shape and constructs a 3-D model of an object using Computer Vision techniques under the condition that position and shape of the object is unknown. 3-D modeling is performed from image streams which are captured by a camera placed on the robot. The results show the effectiveness of our method.

Indoor navigation based on an inaccurate map using object recognition

This paper presents a navigation system based on an inaccurate map. The map we consider here is a hybrid of a topological map and a geometrical map so that it can be built easily and permits changes in object locations. A localization scheme and an object recognition technique are introduced in order to keep the robot pose consistent on such an inaccurate map. To cope with the problem that a path may also be inaccurate on an inaccurate map, the robot corrects the path based on its pose estimated by the localization method while tracking the path. An experiment shows that the robot successfully navigated in an indoor environment, recognizing several kinds of objects.

Motion Planning for a Mobile Manipultor Based on Joint Motions for Error Recovery

This paper describes motion planning for a mobile manipulator which works in real environment. Mobile manipulators that can manipulate an object was developed up to now. We assume that object grasping is not restricted to just one grasp way and robot can move freely as far as it is in its operational area of the object. This paper proposes the motion planning for object grasping under the previous explained conditions. There are two major issues to detect the robot pose. The first problem is pose error of the robot hand. The second problem is kinematical redundancy of the mobile manipulator. The pose is evaluated from the amount of joint motions for recovery of hand pose error. Experiments show the effectiveness of the proposed method.

A SLAM based teleoperation and interface system for indoor environment reconnaissance in rescue activities

It is important and necessary to develop a teleoperated mobile robot to have reconnaissance operation inside of the extreme earthquake stricken building or underground town. The present authors aim at developing such a teleoperated mobile robot. In this paper, a prototype of the teleoperation and man-robot interface system is proposed. The tele-operated robot and the remote operation station is linked with wireless network and the operator can command the robot. One feature point of this system is to provide a live omni-directional panoramic view image and live map to the operator on the display of the remote operation station. The live map represents the floor plan of the moving environment and it is provided by SLAM technology with LRF scan data. It proves very effective to have combined use of the panoramic image and the live map. Experimental set up and integrated experiments are also reported in this paper.

Picking up an Unknown Object through Autonomous Modeling and Grasp Planning by a Mobile Manipulator

This paper describes a novel framework for object picking and carrying task by a mobile manipulator. Conventionally, researches on mobile manipulator cope well with object manipulation task with utilizing predefined knowledge or specific tools. So these researches have an essential problem that a new target object cannot be added without relatively many preparation. On the other hand, in our framework of a robot system, because the robot can create the 3D shape model of the object and can plan a grasp pose for the object autonomously under the condition that only the position of the object is given to the robot. Experimental results show the effectiveness of our robot system to be implemented our proposed method.