Range Sensor Data Research Articles

Elevation moment of inertia: A new feature for Monte Carlo localization in outdoor environment with elevation map

AbstractThe elevation map is one of the most popular maps for outdoor navigation. We propose the elevation moment of inertia (EMOI), which represents the distribution of elevation around a robot in an elevation map, for use in the matching of elevation maps. Using this feature, outdoor localization can be performed with an elevation map without external positioning systems. In this research, the Monte Carlo localization (MCL) method is used for outdoor localization, and the conventional method is based on range matching, which compares range sensor data with the range data predicted from an elevation map. Our proposed method is based on EMOI matching. The EMOI around a robot is compared with the EMOIs for all cells of the pregiven reference elevation map to find a robot pose with respect to the reference map. MCL based on EMOI matching is very fast, although its accuracy is slightly lower than that of conventional range matching. To deal with the disadvantage of EMOI matching, an adaptive switching scheme between EMOI matching and range matching was also proposed. Various outdoor experiments indicated that the proposed EMOI significantly reduced the convergence time of MCL. Therefore, the proposed feature is considered to be useful when an elevation map is used for outdoor localization. © 2010 Wiley Periodicals, Inc.

Skill-Assist Safety and Intelligence Technology

Skill-Assist for automobile manufacturing enables operators to move heavy component modules to target sites, playing a valuable role on production lines. Skill-Assist has high-powered actuators and operates in physical contact with users, so safety is a top priority. This paper describes safety technology developed for and implemented in Skill-Assist at the National Institute of Advanced Industrial Science and Technology, Japan (Skill-Assist@AIST). Risk was assessed for main causes of potentially hazardous events, which were projected to result from abnormal command signals generated by the controller, human error, and unauthorized access. In this paper, we focus on safety measures against abnormal command signals and human error, and introduce current safety technology for Skill-Assist@AIST. Highly reliable control includes a dual-channel controller and fail-safe fault-detection hardware (FSFDH) for ensuring functional safety through command signal monitoring. A reaching-gesture recognition (RGR) algorithm based on laser range sensor data and a hidden Markov model (HMM) predictively detect operator error that outlies predefined safe reaching.

2A2-F03 時間最適化軌道による移動ロボットの実時間走行と自己位置推定

This paper discusses the followings: First, based on an obtained collision-free path by Path Shortcut Method, the velocity continuous time optimal trajectory with specified acceleration constraints is successively obtained by using the proposed time scaling technique. Second, a SLAM method by sequential scan matching of laser range sensor data is proposed on Hough transformation. Third, continuous real-time navigation is realized on parallel processing of both trajectory generation and wheels control by multi-threads programming. Finally, the above proposed methods are effectively confirmed by the navigation of a real mobile robot.

방향각 및 거리 정보에 의한 이동 로봇의 실시간 목표물 추종 방법

This paper presents a method for a mobile robot to follow a moving object in real time. The robot follows a target object keeping the facing angle toward the target and the distance to the target to given value. The method consists of two procedures: first, the detection of target position in the robot coordinate system, and the second, the calculation of translational velocity and rotational velocity to follow the object:. To detect the target location, range sensor data is represented in histogram. Based on the real time calculation of the location of the target relative to the robot, translational velocity and rotational velocity to follow the target are calculated. The velocities make the heading angle and the distance to target converge toward the desired ones. The performance of the method is tested through simulation. In the simulation, the target moves with three different trajectories, straight line trajectory, rectangular trajectory, and circular trajectory. As shown in the results, it is inevitable to lose track temporarily of the target when the target suddenly changes its motion direction. Nevertheless, the robot speeds up to catch up and finally succeeds to follow the target as soon as possible even in this case. The proposed method can also be utilized to coordinate the motion of multiple robots to keep their formation as well as to follow a target.

SLAM in Indoor Environments using Omni-directional Vertical and Horizontal Line Features

An autonomous mobile robot must have the ability to navigate in an unknown environment. The simultaneous localization and map building (SLAM) problem have relation to this autonomous ability. Vision sensors are attractive equipment for an autonomous mobile robot because they are information-rich and rarely have restrictions on various applications. However, many vision based SLAM methods using a general pin-hole camera suffer from variation in illumination and occlusion, because they mostly extract corner points for the feature map. Moreover, due to the narrow field of view of the pin-hole camera, they are not adequate for a high speed camera motion. To solve these problems, this paper presents a new SLAM method which uses vertical lines extracted from an omni-directional camera image and horizontal lines from the range sensor data. Due to the large field of view of the omni-directional camera, features remain in the image for enough time to estimate the pose of the robot and the features more accurately. Furthermore, since the proposed SLAM does not use corner points but the lines as the features, it reduces the effect of illumination and partial occlusion. Moreover, we use not only the lines at corners of wall but also many other vertical lines at doors, columns and the information panels on the wall which cannot be extracted by a range sensor. Finally, since we use the horizontal lines to estimate the positions of the vertical line features, we do not require any camera calibration. Experimental work based on MORIS, our mobile robot test bed, moving at a human's pace in the real indoor environment verifies the efficacy of this approach.

2P1-H06 レーザレンジセンサと画像テンプレートマッチングによる自己位置推定(Localization and Mapping)

We are involved in the development project of mobile toilet robot that assists defecating behavior of the handicapped/aged persons. The current mobile toilet robot has differential drive wheel mechanism, and is equipped with laser range sensors instead of vision sensors. By considering the working space of the mobile toilet, we adopted template matching method for localization where laser range sensor data were converted to vision images and then compared. We experimented on the method for the mobile toilet, and evaluated the localization results.

2P1-C28 半自律移動ロボットの開発 : センサフュージョンによる自己位置推定

Self localization is one of the most important issues for mobile robot control. Mobile robot localization is to estimate the position and a direction of the robot from sensor data. Odometry is generally used for robot localization, but it has a disadvantage as cumulating encoder errors. This paper presents a self localization method use odometry data, Laser Range Sensor (LRS) data and Omni-directional camera data. Self localization by LRS is to revise the self position that is checked LRS data against obstacle information in environment and is estimated from Odometry data. Self localization by Omni-directional camera is to estimate the self position from the distance information that measures distance to a landmark. We realized the self localization method that has highly precision and robustness by fusing two methods.

A parallel algorithm, architecture and FPGA realization for landmark determination and map construction in a planar unknown environment

Good representations of the environment are crucial for navigation of autonomous systems. This paper presents a time and space-efficient hardware-based solution for construction of an environment map based on range sensor data gathered by a robot at P locations in an unknown 2D environment. In particular, the paper proposes a new parallel algorithm based on training a set of K place units for generating “perceptual landmarks” and connecting the landmarks based on sensor information. The computational complexity of this parallel algorithm is O( MP log K) where M is the number of training iterations. The algorithm has been mapped to hardware. The architecture comprises of components such as Content Addressable Memory and Wallace tree adders. The architecture has been implemented in Xilinx Field Programmable Gate Array (FPGA) and results show that the proposed design achieves high speed taking approximately 100 ms to identify 49 landmarks and build a map of an environment of size 3.75 m × 3.75 m. Further, the entire design for an environment with 196 sample points and 49 processing elements fits in one XCV3200E (or XC2V6000) device. The details of the experimental setup for gathering data are also presented.

Morphological surface profile extraction with multiple range sensors

A novel method is described for surface profile extraction based on morphological processing of multiple range sensor data. The approach taken is extremely flexible and robust, in addition to being simple and straightforward. It can deal with arbitrary numbers and configurations of sensors as well as synthetic arrays. The method has the intrinsic ability to suppress spurious readings, crosstalk, and higher-order reflections, and process multiple reflections informatively. The performance of the method is investigated by analyzing its dependence on surface structure and distance, sensor beamwidth, and noise on the time-of-flight measurements.

Telediagnosis and teleinspection potential of telematic techniques

The combination of modern information processing and telecommunication methods offers interesting application potential in the teleservicing of remote sites. This includes operations and maintenance of plants as well as monitoring the state of construction areas from remote centres. This paper presents as concrete example the teleoperations of remote mobile robots in an outdoor environment. Details of the technical remote control realisation on basis of the robot's camera and range sensor data, transferred via inexpensive Internet links are discussed. The application potential of these methods to transfer remote sensor data and to perform control reactions for servicing tasks is outlined.

Neural Networks for Mobile Robot Localisation using Infra-Red Range Sensing

For a robot to be fully autonomous whilst mobile, it is necessary for it to be able to determine its position in its environment. Most of the work on this problem has concentrated on using geometrical techniques which are typically implemented as part of a Kalman filter cycle. This paper examines the possibility of using a neural network to assist in the task of estimating the position of the robot. This is beneficial because it does not require beacons to be placed in the environment or the use of an explicit map of the environment. It does not require knowledge of the previous estimate of the robot’s position. In this paper, Radial Basis Function networks and Multi-Layer Perceptrons are trained to estimate the functional relationship between preprocessed range sensor data and the position of the robot. This approach is assessed using both simulated and real range data.

Localization of a UUV within structures using range data and world modelling techniques

Modern industrial underwater vehicles are being operated in ever increasing complex and hostile environments. Increasing emphasis on safety within the oil industry has led to the advancement of underwater vehicle technology. This paper presents the development work for a prototype localization system for use on an Unmanned Underwater Vehicle (UUV), operating within the vicinity of a structured worksite, e.g. an oil platform. This is an important area of research for such applications as oil platform and wellhead inspection, seabed object identification and wet salvage. A suitable testing rig model was constructed and modelled using the AutoCAD computer-aided design package. Both visual and range sensor data were provided using a one-spot laser triangulation range-finding system. A software tool was developed which made it possible to generate a world model from a DXF file, exported from the AutoCAD construction drawing. Two-dimensional image-processing techniques segmented the camera image's scene, and identified regions of interest upon which to range. The acquired range sensor data from each region were then analysed to create a scene model. This was presented to the cognition system, which analysed the software world model, using a tree-searching algorithm, to determine which features were present in the scene. The position and orientation of the range sensor, with respect to the model, were then determined by applying a 3D geometrical solution search around the scene's matched features. The resulting range sensor positional information was then fed back to AutoCAD, which displayed a representation of the sceneextracted from the world model. The research has demonstrated that provided the system was supplied with sufficient visual and range sensor data, then an accurate visual estimate of vehicle position was possible.

A multi-sensor approach to creating accurate virtual environments

Creating virtual environment models often requires geometric data from range sensors as well as photometric data from CCD cameras. The model must be geometrically correct, visually realistic, and small enough in size to allow real-time rendering. We present an approach based on 3D range sensor data, multiple CCD cameras, and a colour high-resolution digital still camera. The multiple CCD cameras provide images for a photogrammetric bundle adjustment with constraints. The results of the bundle adjustments are used to register the 3D images from the range sensor in one coordinate system. The images from the high-resolution still camera provide the texture for the final model. The paper describes the system, the techniques for the registration of the 3D images, the building of the efficient geometric model, and the registration and integration of the texture with a simplified geometric model.

Implicit Surface-Based Geometric Fusion

This paper introduces a general purpose algorithm for reliable integration of sets of surface measurements into a single 3D model. The new algorithm constructs a single continuous implicit surface representation which is the zero-set of a scalar field function. An explicit object model is obtained using any implicit surface polygonization algorithm. Object models are reconstructed from both multiple view conventional 2.5D range images and hand-held sensor range data. To our knowledge this is the first geometric fusion algorithm capable of reconstructing 3D object models from noisy hand-held sensor range data.This approach has several important advantages over existing techniques. The implicit surface representation allows reconstruction of unknown objects of arbitrary topology and geometry. A continuous implicit surface representation enables reliable reconstruction of complex geometry. Correct integration of overlapping surface measurements in the presence of noise is achieved using geometric constraints based on measurement uncertainty. The use of measurement uncertainty ensures that the algorithm is robust to significant levels of measurement noise. Previous implicit surface-based approaches use discrete representations resulting in unreliable reconstruction for regions of high curvature or thin surface sections. Direct representation of the implicit surface boundary ensures correct reconstruction of arbitrary topology object surfaces. Fusion of overlapping measurements is performed using operations in 3D space only. This avoids the local 2D projection required for many previous methods which results in limitations on the object surface geometry that is reliably reconstructed. All previous geometric fusion algorithms developed for conventional range sensor data are based on the 2.5D image structure preventing their use for hand-held sensor data. Performance evaluation of the new integration algorithm against existing techniques demonstrates improved reconstruction of complex geometry.

Modeling of facial expression and emotion for human communication system

The goal of this research is to realize a face-to-face communication environment with machine by giving a facial expression to computer system. In this paper, modeling methods of facial expression including 3D face model, expression model and emotion model are presented. Facial expression is parameterized with Facial Action Coding System (FACS) which is translated to the grid's motion of face model which is constructed from the 3D range sensor data. An emotion condition is described compactly by the point in a 3D space generated by a 5-layered neural network and its evaluation result shows the high performance of this model.

Dynamic Environment's Range Data Analysis by a Moving Observer

The use of mobile robots in dynamic environment is constrained by their environment sensing capabilities. Especially for motion execution and local trajectory planning, data about the egomotion of the robot and data about the motion of dynamic objects in the vicinity are necessary. This paper gives an overview of range sensor systems and data analysis methods which are used to perform detection of egomotion on one hand and estimation of motion of other objects on the other hand. The outline of a new method for motion estimation of a moving observer in the presence of moving objects is presented.

Mobile Path Planning: A Hierarchical Approach

This paper presents the approach taken by the UK National Advanced Robotics Research Centre to develop an advanced mobile vehicle. The method of fusing the vehicle's range sensor data into a mapof the surrounding environment in real-time is discussed. This map is dynamically updated as the vehicle moves and fonns the data used by the hierarchical path planner to control the movement of the vehicle

A System for Autonomous Cross Country Navigation

Autonomous Cross-Country Navigation requires a system which can support a rapid traverse across challenging terrain while maintaining vehicle safety. This work describes a system for autonomous cross country navigation as implemented on the NavLab II, a computer-controlled off-road vehicle at Carnegie Mellon. The navigation software is discussed. The perception subsystem constructs digital maps of the terrain from range sensor data in real time. The planning subsystem uses a generate-and-test scheme to find safe trajectories through the map, and validates them through simulated driving. The planning process considers both kinematic and dynamic constraints on vehicle motion. The system was successful in achieving 300m autonomous runs on moderate terrain at speeds up to 4.25 m/s.

Position estimator for underground mine equipment

A 2-D perception system that exploits the accuracy and resolution of a laser range sensor to determine the position and orientation of a mobile robot in a mine environment is described. The perception system detects features from range sensor data and matches the features to a map of the mine to compute the sensor position. The features used are line segments and corners, which represent the typical geometry of the mine walls and intersections found in room-and-pillar-type mining. The position estimate is refined by minimizing the error between the map and sensed features. This position information can be used for autonomous navigation when a map of the mine is available or to survey the mine in order to build such a map. The technique is applied to robot navigation in a mine mockup. A refinement of this system could guide machines to yield productive, safe mining operations.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>