Laser Range Finder Sensor Research Articles

Accumulator-free Hough Transform for Sequence Collinear Points

The perception, localization, and navigation of its environment are essential for autonomous mobile robots and vehicles. For that reason, a 2D Laser rangefinder sensor is used popularly in mobile robot applications to measure the origin of the robot to its surrounding objects. The measurement data generated by the sensor is transmitted to the controller, where the data is processed by one or multiple suitable algorithms in several steps to extract the desired information. Universal Hough Transform (UHT) is one of the appropriate and popular algorithms to extract the primitive geometry such as straight line, which later will be used in the further step of data processing. However, the UHT has high computational complexity and requires the so-called accumulator array, which is less suitable for real-time applications where a high speed and low complexity computation is highly demanded. In this study, an Accumulator-free Hough Transform (AfHT) is proposed to reduce the computational complexity and eliminate the need for the accumulator array. The proposed algorithm is validated using the measurement data from a 2D laser scanner and compared to the standard Hough Transform. As a result, the extracted value of AfHT shows a good agreement with that of UHT but with a significant reduction in the complexity of the computation and the need for computer memory.

Cemetery Mapping and Digital Data Analysis: A Case Study in Minnesota, USA

This study examines how geospatial technologies can be used in the aid of local-level cemetery management with limited resources using a case study in Woodland Hills Memorial Park Cemetery, Minnesota, USA. The hard-copy records in a handwritten ledger were manually transferred into an Excel table. The spatial data of the gravesites were collected using a Trimble Geo 7X unit with a Zephyr antenna and a Laser Rangefinder sensor over the summer of 2017. A geodatabase was constructed by joining the Excel table with the GPS data in GIS. A procedure was also developed to map the spatial distributions of plots and analyze the demographic data. It was demonstrated that a very high locational accuracy could be achieved based on carefully designed GPS data collection strategies. In addition, the data analysis results revealed that there were 12,190 plots in total, approximately half of which were still available for purchase. Among the 5,906 inhabitants buried at the Woodland Hills, many were ethnically German and Scandinavian, of whom 9.7% were veterans and nearly half were from the Greatest Generation (born between 1901 and 1927). The birth, death, and age distributions are significantly different between the nonveteran and veteran groups. Clustered patterns were identified for the filled plots and all the Generation categories. Such results will be beneficial to local cemetery managers to plan for further development as well as to future historians or individuals interested in the local culture and history. The proposed methods can greatly facilitate local-level cemetery data collection, mapping, query, and analysis.

Analysis of Mobile Robot Indoor Mapping using GMapping Based SLAM with Different Parameter

Mapping is one of the elemental application of the mobile robot. The map is created using the mobile robot by employing sensors such as camera, sonar and laser sensor. One of the most popular mapping methods is the Simultaneous Localization and Mapping (SLAM). SLAM allows the map to be created while localizing the robot location in the map at the same time. GMapping is one of the widely used algorithms in SLAM which will be used in this project. The mobile robot is equipped with a Hokuyo Laser Range Finder sensor and netbook. The router is used for wireless communication between the mobile robot and the user. The GMapping is done in two different locations of different lab size and amount of features in the area. Three trial is conducted to investigate the effects of different parameters such as robot speed, mapping delay and particle filter on the mapping quality. The results show a significant difference in terms of mapping accuracy and the time taken to complete the process as the parameter changed from the three trial. As a result, the parameter used in the second trial, robot speed 0.1333m/s, mapping delay 1s and particle filter 30 is considered as the best based on the time taken and the map accuracy.

A Localizability Constraint-Based Path Planning Method for Autonomous Vehicles

During autonomous navigation, environmental information and map noises at different locations may have dissimilar influence on a vehicle’s localization process. This implies that the vehicle’s localizability, i.e., the ability to localize itself in an environment using laser range finder (LRF) readings, varies over a given map. It is essential to take this factor into consideration when planning a path to avoid large localization errors or placing the vehicle at risk of failure to perform localization. We propose a localizability constraint (LC)-based path planning method for autonomous vehicles which plans the navigation path according to LRF sensor model of the vehicle in an effort to maintain a satisfactory level of localizability throughout the path, as well as to reduce the overall localization error. Our method is not limited to any specific algorithm in the optimization stage. Paths planned with and without LC are compared, and the influence of the LRF sensor model on planning outcomes is discussed through simulations. By conducting comparative experiments on a “JiaoLong” intelligent wheelchair in both indoor and outdoor environments, we show that the proposed method effectively lowers the localization error along the planned paths.

Human-Machine Interface for a Smart Wheelchair

The paper describes the integration of hardware and software with sensor technology and computer processing to develop the next generation intelligent wheelchair. The focus is a computer cluster design to test high performance computing for smart wheelchair operation and human interaction. The LabVIEW cluster is developed for real-time autonomous path planning and sensor data processing. Four small form factor computers are connected over a Gigabit Ethernet local area network to form the computer cluster. Autonomous programs are distributed across the cluster for increased task parallelism to improve processing time performance. The distributed programs operating frequency for path planning and motion control is 50Hz and 12.3Hz for 0.3 megapixel robot vision system. To monitor the operation and control of the distributed LabVIEW code, network automation is integrated into the cluster software along with a performance monitor. A link between the computer motion control program and the wheelchair joystick control of the drive train is developed for the computer control interface. A perception sensor array and control circuitry is integrated with the computer system to detect and respond to the wheelchair environment. Multiple cameras are used for image processing and scanning laser rangefinder sensors for obstacle avoidance in the cluster program. A centralized power system is integrated to power the smart wheelchair along with the cluster and sensor feedback system. The on board computer system is evaluated for cluster processing performance for the smart wheelchair, incorporating camera machine vision and LiDAR perception for terrain obstacle detection, operating in urban scenarios.

FFT-Based Scan-Matching for SLAM Applications with Low-Cost Laser Range Finders

Simultaneous Localization and Mapping (SLAM) is an active area of robot research. SLAM with a laser range finder (LRF) is effective for localization and navigation. However, commercial robots usually have to use low-cost LRF sensors, which result in lower resolution and higher noise. Traditional scan-matching algorithms may often fail while the robot is running too quickly in complex environments. In order to enhance the stability of matching in the case of large pose differences, this paper proposes a new method of scan-matching mainly based on Fast Fourier Transform (FFT) as well as its application with a low-cost LRF sensor. In our method, we change the scan data within a range of distances from the laser to various images. FFT is applied to the images to determine the rotation angle and translation parameters. Meanwhile, a new kind of feature based on missing data is proposed to determine the rough estimation of the rotation angle under some representative scenes, such as corridors. Finally, Iterative Closest Point (ICP) is applied to determine the best match. Experimental results show that the proposed method can improve the scan-matching and SLAM performance for low-cost LRFs in complex environments.

Tasarlanmış Engebeli Ortamda Otonom Mobil Robot Gezinimi

Main study areas for robotics research can be given as: mapping, localization, navigation and exploration. Given a robot’s current position, partial map of the environment and a goal position; navigation problem can be defined as optimal path planning and path following. Path planning and path following problem should be handled according to environment being static or dynamic, robot's mobility capabilities, sensors used on the robot and the roughness of the environment. In the study a four wheeled, skid-steering robot with laser range finder and depth sensor is built for Gazebo simulation environment. Also a statically structured labyrinth that consists of 15 degree continuous ramps, 15 degree discontinuous ramps, amorphous holes that robot cannot autonomously escape from if fallen into, walls and discontinuous obstacles that are below the robot laser height. 2D simultaneous localization and mapping, 3D mapping, path planning and path following with respect to the 3D map are implemented on Robot Operating System (ROS). Optimal path planning in rough terrain is accomplished by combining A* heuristic with a function of height difference of the 3D map nodes. Path following is carried out by turning-to and moving-towards actions on each sequential path node pairs. Tests performed on the labyrinth shows that obstacle avoidance, path planning and path following can be carried out successfully with the given implementation.

Information-Fusion Methods Based Simultaneous Localization and Mapping for Robot Adapting to Search and Rescue Postdisaster Environments

The first application of utilizing unique information-fusion SLAM (IF-SLAM) methods is developed for mobile robots performing simultaneous localization and mapping (SLAM) adapting to search and rescue (SAR) environments in this paper. Several fusion approaches, parallel measurements filtering, exploration trajectories fusing, and combination sensors’ measurements and mobile robots’ trajectories, are proposed. The novel integration particle filter (IPF) and optimal improved EKF (IEKF) algorithms are derived for information-fusion systems to perform SLAM task in SAR scenarios. The information-fusion architecture consists of multirobots and multisensors (MAM); multiple robots mount on-board laser range finder (LRF) sensors, localization sonars, gyro odometry, Kinect-sensor, RGB-D camera, and other proprioceptive sensors. This information-fusion SLAM (IF-SLAM) is compared with conventional methods, which indicates that fusion trajectory is more consistent with estimated trajectories and real observation trajectories. The simulations and experiments of SLAM process are conducted in both cluttered indoor environment and outdoor collapsed unstructured scenario, and experimental results validate the effectiveness of the proposed information-fusion methods in improving SLAM performances adapting to SAR scenarios.

Extrinsic Calibration of Camera and 2D Laser Sensors without Overlap

Extrinsic calibration of a camera and a 2D laser range finder (lidar) sensors is crucial in sensor data fusion applications; for example SLAM algorithms used in mobile robot platforms. The fundamental challenge of extrinsic calibration is when the camera-lidar sensors do not overlap or share the same field of view. In this paper we propose a novel and flexible approach for the extrinsic calibration of a camera-lidar system without overlap, which can be used for robotic platform self-calibration. The approach is based on the robot–world hand–eye calibration (RWHE) problem; proven to have efficient and accurate solutions. First, the system was mapped to the RWHE calibration problem modeled as the linear relationship , where and are unknown calibration matrices. Then, we computed the transformation matrix , which was the main challenge in the above mapping. The computation is based on reasonable assumptions about geometric structure in the calibration environment. The reliability and accuracy of the proposed approach is compared to a state-of-the-art method in extrinsic 2D lidar to camera calibration. Experimental results from real datasets indicate that the proposed approach provides better results with an L2 norm translational and rotational deviations of 314 mm and respectively.

Proposal of Lateral Inhibition Real Time Map Updating Method for Simultaneous Localization and Mapping by Using LRF

In this paper, a novel real time map updating algorithm using a concept of lateral inhibition was proposed and it was evaluated by simple 2D room mapping experiment. For long time and difficult map creation process of simultaneous localization and mapping (SLAM) algorithm, stable map updating is one of the important topics. By using the lateral inhibition process to the map updating process, it is able to reduce the complexity of the map updating process of the SLAM. It could reduce the noise around the wall and emphasize the border (such a wall). The experimental result of 6.0 x 3.5 m 2D room with many obstacles shows the x-y positional and the rotational estimation precision would be measured as Δx, y= 0±0 m (within laser range finder measurement error, ±S.D.) and the θ= - 3.3×10-4 ±1.4×10-3 rad (1 hour measurement), and the map was updated real time when the laser range finder sensor was moving from a position to another room position with low speed. In addition, the environment map was created without deformation in the three trees environment which has a little wind (<1m/sec) and uneven shape. The long time stable position and rotation estimation could be realized by using the lateral inhibition mapping update algorithm.

Human-like Artificial Intelligent Wheelchair Robot Navigated by Multi-Sensor Models in Indoor Environments and Error Analysis

Intelligent mobile robot navigation in indoor environments is still a challenge. In this paper, we propose a method in which the wheelchair robot imitates human like navigation by interacting with the surrounding environments. Two types of sensor data are used to train neural networks, which are later used to control the robot to reach the goal location in different indoor environments. The robot navigates from the start to the goal location in the environments with obstacles. In first model, we used the Laser Range Finder (LRF) sensor data as input of the neural network. In the second model in addition to the LRF data, the processed camera sensor data are also utilized. We compare the performance of two neural networks models by analyzing the error between the human and neural network based real robot navigations. The experimental results show that our proposed models are efficient for mobile robot navigations. In addition, errors are analyzed in this paper.

Robust 2D map building with motion-free ICP algorithm for mobile robot navigation

SUMMARYA new motion-free iterative closest point (ICP) algorithm is proposed for building a two-dimensional (2D) map for mobile robot navigation. A laser range finder (LRF) sensor is installed on a mobile robot to scan and measure the depth data of the environment to form a 2D map during mobile robot navigation. Because the scanning and navigation motions are performed independently, the scanned data contain distortions from the motions of the mobile robot. To compensate for the distortions, the proposed motion-free ICP algorithm estimates the effects of the dynamic motions of the robot on the scanning process. That is, the motion-free algorithm compensates for the distance measurement errors related to the dynamic changes in the mobile robot's velocity. Experiments were performed with actual velocity changes of a mobile robot to demonstrate and verify the effective performance of the proposed algorithm.

A Simplified Solution to Motion Estimation Using an Omnidirectional Camera and a 2-D LRF Sensor

Recently, omnidirectional camera-based motion estimation has been improved as a result of planar-motion assumption, which helps reduce the computational time. In practice in outdoor terrains, vehicle motion does not satisfy this assumption. Motivated by this problem, this paper proposes a method that uses the minimal set of parameters of geometric constraint for estimating the pseudo-three-dimensional motion of the vehicle based on an omnidirectional camera and a laser range finder (LRF). To reduce the number of the parameters for accelerating the computational speed, it is supposed that the vehicle moves under the nonholonomic four-wheel motion model, which requires constraints between rotation and translation components. The method consists of two stages. First, the LRF is used to estimate the orientation and the translation magnitude of the vehicle movement. Second, a pair of corresponding points in sequential images is used along with the results of the first stage to estimate the motion of the vehicle. Furthermore, this paper also presents a closed-form solution for problem solving. The experimental results, using synthetic and real data in different terrain conditions, demonstrate that the proposed method provides the higher accuracy with the lower computational time as compared with the state-of-the-art methods.

Simplified EKF-SLAM by Combining Laser Range Sensor with Retro-Reflective Markers for Use in Kindergarten

Recently in Japan, work contents imposed to childminders have increased due to several reasons such as variety of children's growth stage, requirement from parents, and administrative advices. To maintain good childcare service and to support kindergarten staffs, an objective monitoring system to watch over children is expected. In this paper, for the monitoring system, an automatic method to make an indoor map using the SLAM technique is discussed. To solve problem specific to the kindergarten use, using special landmark (LM) markers consisting of retro reflective material, an approach of a simplified EKF-SLAM which is effective by short-distance survey is proposed. Thorough simulation, the SLAM algorithm with an extraction method of the special LM from the laser range finder (LRF) sensor data was verified, and it was confirmed that the estimation error was as small as 0.57% against total length of robot locomotion. Same algorithm was implemented to an actual robot that was mounted with the LRF sensor, and practical effectiveness was validated. As a result, the ability of the presented SLAM with a margin of estimation error of 5.3% was also confirmed in the experiment.

Inside Vehicle Inspection System Utilizing a Mobile Robot with LRF Sensor

Automated vehicle inspection is utilized to inspect vehicles in a parking lots. This paper presents an approach for inspecting vehicle inside through car windows to determine any changes since the last inspection using a Laser Range Finder (LRF) sensor. Features of the approach include the detection inside vehicles method, data alignment using an ICP algorithm, inside vehicle data comparison to find any differences since the last inspection. An item identification method has been used to obtain the average height and size of objects inside vehicles to identify such changes. Our approach was shown to successfully detect typical simple items, i.e., bags, notebook PCs, and wallets, used to test the proposed method. Experiments are conducted to demonstrate the efficiency of our approach for inspecting and recognizing objects inside vehicles.

Optimal Control of the Passive and Novel Docking Mechanism

We propose a novel passive docking and exchanging mechanism of batteries at mobile robots. New proposed mechanism needs no actuators and sensors such as laser range finder and vision sensor to align. This mechanism, key and housing system, reduces the docking, exchanging time of batteries, and contact force by complete passive mechanism and applying optimal control of velocity by Pontryagin's minimum principle. This superior performance is demonstrated by dynamic analysis program, Recurdyn and Matlab Simulink.

General Cartridge Shape Detection System Design

According to present question about low precision detection of cartridge's geometric shape and not high level of automatic detection ,designer has designed multiple detection system about geometric parameters of the cartridge .Use the optical triangulation as a basic theory, and measure the cartridge's shapewall thicknessbottom thickness and so on at the same time by adopting Laser Range Finder Sensor with d high level of precision. Adopt servo motor to drive detection arm to realize the generality of all kinds of cartridge's detection. When test system collect data in real-time to make a report, it use a OPENGL tool to create the 3d graphics of objects being detected and see their defects directly as well as analysiscalculate the system's testing error. Experiment shows, that this method's fasterhigher level of detection can meet the demands of cartridge's geometric parameters.

Vehicle localization system using offboard range sensor network

Most mobile vehicle localization approaches rely on onboard sensors. However, whenever the installation of sensors onboard the vehicle is unfeasible, an alternative solution is to install them in the environment. One such environment is the ITER nuclear fusion reactor, where all maintenance operations have to be performed by remote handling, due to the radiation levels. This paper addresses the problem of vehicle localization in a structured environment, using a network of laser range finder sensors. The approach taken is based on: (1) the optimization of the sensor placement in the environment, aiming at the maximization of the area covered by the sensors and the redundancy of the sensor network, and (2) a probabilistic approach for vehicle localization. Two localization methods were evaluated: Extended Kalman Filter and Particle Filter. These two methods are compared, with respect to localization performance and robustness, both in simulation and using a real vehicle in a mock-up scenario.

Development of a Mobile Robot for Transport Application in Hospital

We have been developing the Muratec Keio Robot (MKR), an autonomous omnidirectional mobile robot system for transport applications in the hospital domain. This robot has a chamber to transfer luggage, specimens, and other materials safely. The robot has sensor devices such as a stereocamera, laser range finder and ultrasonic sensors to recognize surrounding environment and user interfaces such as a touch panel display and a display scrolling messages for interaction with human beings. Experiments are carried out at hospitals to verify the performance of elemental MKR-003 technology. Experimental results confirmed that the robot canmove without colliding with static or moving obstacles in a hospital. This paper presents the omnidirectional mobile transfer mechanisms, bumper switch, elevator passenger system and transport information systems.

복합 바퀴-다리 이동형 로봇의 저전력 보행 기반 장애물 회피 알고리즘

최근 다양한 환경에서 지능형 로봇의 안정된 이동방법에 대한 연구가 활발히 이루어지고 있다. 본 논문에서는 복합 바퀴-다리형 이동 로봇의 설계 방법을 제안하고, LRF(Laser Range Finder)센서를 이용한 복합 이동형 로봇의 저전력 보행 기반 장애물 회피 알고리즘을 제안하였다. 로봇의 자세 안정화와 소비에너지를 줄이기 위해 모터 전류값을 바탕으로 자세를 보정하여 저전력 보행 알고리즘을 구현하였고, 이를 기반으로 LRF센서를 이용한 장애물 회피 알고리즘을 제안하였다. 로봇의 각 다리에서 소비되는 전력을 고르게 분포시키게 자세를 보정하여 보행을 안정화시키고, 최단 경로로 장애물을 회피하여 이동함으로써 전체 소비 에너지를 감소시키며 보행 안정성을 향상하였다. 본 연구에서 제안한 방법들은 실제 복합 이동 로봇의 보행 및 장애물 회피 실험을 통해 성능을 검증하였다. There are many researches to develop robots that improve its mobility to adapt in various uneven environments. In the paper, a hybrid wheeled and legged mobile robot is designed and a obstacle avoidance algorithm is proposed based on low power walking using LRF(Laser Range Finder). In order to stabilize the robot's motion and reduce energy consumption, we implement a low-power walking algorithm through comparison of the current value of each motors and correction of posture balance. A low-power obstacle avoidance algorithm is proposed by using LRF sensor. We improve walking stability by distributing power consumption and reduce energy consumption by selecting a shortest navigation path of the robot. The proposed methods are verified through walking and navigation experiments with the developed hybrid robot.