Eijiro Takeuchi

Bio: Eijiro Takeuchi is an academic researcher from Nagoya University. The author has contributed to research in topics: Point cloud & Mobile robot. The author has an hindex of 20, co-authored 105 publications receiving 2055 citations. Previous affiliations of Eijiro Takeuchi include Toyota & Tohoku University.

An Open Approach to Autonomous Vehicles

Abstract: Autonomous vehicles are an emerging application of automotive technology. They can recognize the scene, plan the path, and control the motion by themselves while interacting with drivers. Although they receive considerable attention, components of autonomous vehicles are not accessible to the public but instead are developed as proprietary assets. To facilitate the development of autonomous vehicles, this article introduces an open platform using commodity vehicles and sensors. Specifically, the authors present algorithms, software libraries, and datasets required for scene recognition, path planning, and vehicle control. This open platform allows researchers and developers to study the basis of autonomous vehicles, design new algorithms, and test their performance using the common interface.

Collaborative mapping of an earthquake-damaged building via ground and aerial robots

Abstract: We report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake-damaged building. The goal of the experimental exercise is the generation of three-dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics. © 2012 Wiley Periodicals, Inc. (This work builds upon the conference paper (Michael et al., 2012).)

A 3-D Scan Matching using Improved 3-D Normal Distributions Transform for Mobile Robotic Mapping

Abstract: A 3D scan matching is an important component for sensor based localization and mapping by a mobile robot in natural environment. In this paper, the present authors propose a way to extend 2D normal distributions transform (NDT) scan matching method to 3D scan matching, and its improvement for faster processing time. This scan matching method divides scan into voxels, and approximates scan points in each cell into normal distribution. That matching time is O(N) with N of the number of input scan points. The authors describe in this paper, NDT for 3D scan points, its acceleration using the dual resolutions of NDT, and experiments of map building in large scale environments

Collaborative Mapping of an Earthquake Damaged Building via Ground and Aerial Robots.

Abstract: We report recent results from field experiments conducted with a team of ground and aerial robots engaged in the collaborative mapping of an earthquake-damaged building. The goal of the experimental exercise is the generation of three-dimensional maps that capture the layout of a multifloor environment. The experiments took place in the top three floors of a structurally compromised building at Tohoku University in Sendai, Japan that was damaged during the 2011 Tohoku earthquake. We provide details of the approach to the collaborative mapping and report results from the experiments in the form of maps generated by the individual robots and as a team. We conclude by discussing observations from the experiments and future research topics. © 2012 Wiley Periodicals, Inc. (This work builds upon the conference paper (Michael et al., 2012).)

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Abstract: Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art is improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions including localization, mapping, perception, planning, and human machine interfaces, were thoroughly reviewed. Furthermore, many state-of-the-art algorithms were implemented and compared on our own platform in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS development.

Scan registration for autonomous mining vehicles using 3D-NDT

Abstract: Scan registration is an essential sub-task when building maps based on range finder data from mobile robots. The problem is to deduce how the robot has moved between consecutive scans, based on the ...

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Abstract: In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

Self-driving cars: A survey

Abstract: We survey research on self-driving cars published in the literature focusing on autonomous cars developed since the DARPA challenges, which are equipped with an autonomy system that can be categorized as SAE level 3 or higher. The architecture of the autonomy system of self-driving cars is typically organized into the perception system and the decision-making system. The perception system is generally divided into many subsystems responsible for tasks such as self-driving-car localization, static obstacles mapping, moving obstacles detection and tracking, road mapping, traffic signalization detection and recognition, among others. The decision-making system is commonly partitioned as well into many subsystems responsible for tasks such as route planning, path planning, behavior selection, motion planning, and control. In this survey, we present the typical architecture of the autonomy system of self-driving cars. We also review research on relevant methods for perception and decision making. Furthermore, we present a detailed description of the architecture of the autonomy system of the self-driving car developed at the Universidade Federal do Espirito Santo (UFES), named Intelligent Autonomous Robotics Automobile (IARA). Finally, we list prominent self-driving car research platforms developed by academia and technology companies, and reported in the media.

Autonomous vehicle perception: The technology of today and tomorrow

Abstract: Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.