Author name cluster

Kiyoshi Irie

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

7 papers

1 author row

IROS Conference 2015 Conference Paper

A dependence maximization approach towards street map-based localization

Kiyoshi Irie
Masashi Sugiyama
Masahiro Tomono

In this paper, we present a novel approach to 2D street map-based localization for mobile robots that navigate mainly in urban sidewalk environments. Recently, localization based on the map built by Simultaneous Localization and Mapping (SLAM) has been widely used with great success. However, such methods limit robot navigation to environments whose maps are prebuilt. In other words, robots cannot navigate in environments that they have not previously visited. We aim to relax the restriction by employing existing 2D street maps for localization. Finding an exact match between sensor data and a street map is challenging because, unlike maps built by robots, street maps lack detailed information about the environment (such as height and color). Our approach to coping with this difficulty is to maximize statistical dependence between sensor data and the map, and localization is achieved through maximization of a Mutual Information-based criterion. Our method employs a computationally efficient estimator of Squared-loss Mutual Information through which we achieved near real-time performance. The effectiveness of our method is evaluated through localization experiments using real-world data sets.

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IROS Conference 2013 Conference Paper

Road recognition from a single image using prior information

Kiyoshi Irie
Masahiro Tomono

In this study, we present a novel road recognition method using a single image for mobile robot navigation. Vision-based road recognition in outdoor environments remains a significant challenge. Our approach exploits digital street maps, the robot position, and prior knowledge of the environment. We segment an input image into superpixels, which are grouped into various object classes such as roadway, sidewalk, curb, and wall. We formulate the classification problem as an energy minimization problem and employ graph cuts to estimate the optimal object classes in the image. Although prior information assists recognition, erroneous information can lead to false recognition. Therefore, we incorporate localization into our recognition method to correct errors in robot position. The effectiveness of our method was verified through experiments using real-world urban datasets.

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ICRA Conference 2012 Conference Paper

Localization and road boundary recognition in urban environments using digital street maps

Kiyoshi Irie
Masahiro Tomono

In this study, we aim to achieve autonomous navigation for robots in environments that they have not previously visited. Many of the existing methods for autonomous navigation require a map to be built beforehand, typically by manually navigating the robot. Navigation without maps, i. e. , without any prior information about the environment, is very difficult. We propose to use existing digital street maps for autonomous navigation. Nowadays digital street maps (e. g. , those provided by Google Maps) are widely available and used routinely. Reuse of existing maps for robots eliminates extra cost of building maps. One of the difficulties in using existing street maps is data association between a robot's observation and the map, because the physical entities that correspond to the boundary lines in the map are unknown. We address this issue by using region annotations such as roads and buildings and prior knowledge. We introduce a probabilistic framework that simultaneously estimates a robot's position and the road's boundaries. We evaluated our method in complex urban environments. Our method successfully localized in environments that includes both roadways and pedestrian walkways.

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ICRA Conference 2011 Conference Paper

3D laser scanner with gazing ability

Tomoaki Yoshida
Kiyoshi Irie
Eiji Koyanagi
Masahiro Tomono

This paper presents a 3D laser scanner that can gaze at an arbitrary region. By modulating the secondary rotation speed of roundly swinging 3D laser scanner, it gazes at a specific region and measures with high density measurement points. The proposed method uses a secondary rotation motor to control the measurement point density and no extra motor is required. The 3D scanner can keep the field of view of and scan cycle time while gazing at any region. The system is evaluated on experimental targets for different scenarios using a mobile robot.

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ICRA Conference 2011 Conference Paper

A High Dynamic Range vision approach to outdoor localization

Kiyoshi Irie
Tomoaki Yoshida
Masahiro Tomono

We propose a novel localization method for outdoor mobile robots using High Dynamic Range (HDR) vision technology. To obtain an HDR image, multiple images at different exposures is typically captured and combined. However, since mobile robots can be moving during a capture sequence, images cannot be fused easily. Instead, we generate a set of keypoints that incorporates those detected in each image. The position of the robot is estimated using the keypoint sets to match measured positions with a map. We conducted experimental comparisons of HDR and auto-exposure images, and our HDR method showed higher robustness and localization accuracy.

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IROS Conference 2010 Conference Paper

A sensor platform for outdoor navigation using gyro-assisted odometry and roundly-swinging 3D laser scanner

Tomoaki Yoshida
Kiyoshi Irie
Eiji Koyanagi
Masahiro Tomono

This paper proposes a light-weight sensor platform that consists of gyro-assisted odometry and a 3D laser scanner for localization of human-scale robots. The gyro-assisted odometry provides highly accurate positioning only by dead-reckoning. The 3D laser scanner has a wide field of view and uniform measuring-point distribution. Robust and computationally inexpensive localization is implemented on the sensor platform using a particle filter on a 2D grid map generated by projecting 3D points on to the ground. The system uses small and low-cost sensors, and can be applied to a variety of mobile robots in human-scale environments. Outdoor navigation experiments were performed at the Tsukuba Challenge 2009, which is an open proving ground for human-scale robots. Our robot successfully navigated the assigned 1-km course in a fully autonomous mode multiple times.

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IROS Conference 2010 Conference Paper

Mobile robot localization using stereo vision in outdoor environments under various illumination conditions

Kiyoshi Irie
Tomoaki Yoshida
Masahiro Tomono

This paper proposes a new localization method for outdoor navigation using a stereo camera only. Vision-based navigation in outdoor environments is still challenging because of large illumination changes. To cope with various illumination conditions, we use 2D occupancy grid maps generated from 3D point clouds obtained by a stereo camera. Furthermore, we incorporate salient line segments extracted from the ground into the grid maps. This grid map building is not much affected by illumination conditions. On the grid maps, the robot poses are estimated using a particle filter that combines visual odometry and map-matching. Experimental results showed the effectiveness and robustness of the proposed method under various weather and illumination conditions.

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