Search

We are developing a robot that can play an outdoor game with children. In realizing such a robot, the person detection and tracking methods play an important role. In this paper, we propose methods for improving person detection and tracking using a laser range finder (LRF). A problem facing player detection using an LRF during play is that detection of a player’s body sometimes fails because of the posture of the player. Thus, we propose two improvements of the conventional person detection algorithm. One is to determine the body boundary when an arm overlaps the body, and the other is to choose the most probable object as the body among the detected objects. Another problem is how to track players who are hidden by other players. We introduce a new algorithm to robustly track a player even when the player is behind other players. We conducted three experiments. In the first experiment, we investigate the performance improvement by the proposed player detection algorithm. The second experiment proves that the proposed player-tracking algorithm can robustly track a player even after the LRF has lost that player. In the last experiment, we conduct the game Darumasan ga Koronda using the developed system and confirm that the system can perform the game without large problems.

This paper proposes a novel multi-sensor-based indoor global localization system integrating visual localization aided by CNN-based image retrieval with a probabilistic localization approach. The global localization system consists of three parts: coarse place recognition, fine localization and re-localization from kidnapping. Coarse place recognition exploits a monocular camera to realize the initial localization based on image retrieval, in which off-the-shelf features extracted from a pre-trained Convolutional Neural Network (CNN) are adopted to determine the candidate locations of the robot. In the fine localization, a laser range finder is equipped to estimate the accurate pose of a mobile robot by means of an adaptive Monte Carlo localization, in which the candidate locations obtained by image retrieval are considered as seeds for initial random sampling. Additionally, to address the problem of robot kidnapping, we present a closed-loop localization mechanism to monitor the state of the robot in real time and make adaptive adjustments when the robot is kidnapped. The closed-loop mechanism effectively exploits the correlation of image sequences to realize the re-localization based on Long-Short Term Memory (LSTM) network. Extensive experiments were conducted and the results indicate that the proposed method not only exhibits great improvement on accuracy and speed, but also can recover from localization failures compared to two conventional localization methods.

The objective of this article is to propose data processing from laser range finder that will provide simple, fast, and reliable object recognition including moving objects. The whole method is based on four steps: segmentation, simplification, correspondence between consequent measurements, and object classification. Segmentation uses raw data from laser range finder and it is significant in logical connection of related segments. The most important step is simplification which provides data reduction and acceleration of object classification. The output of simplification is an object represented by significant points. Correspondence between consequent measurements is based on kd-tree nearest neighborhood search. The object is then classified by its spatial changes. These changes are evaluated by position of correspondent significant points. Input of proposed procedure is a probabilistic model of laser range finder. In this article, versatile probabilistic model of Hokuyo URM-30 LX was used. The method was verified by simulations and by tests in real environment. The results show that proposed method is reliable and with small modifications (of parameters), it is usable with any other planar laser range finder.

The introductory part of the paper briefly explains factors of armed struggle and describes in more detail the technical factor involving the weapon efficiency increase obtained through the improvement of the sighting device, where the laser range finder is applied. Presented are some characteristics of the laser range finder which is an integral part of the M-84 fire control system. The basic concept of the laser range finder is analyzed. Special attention is paid to the analysis of the characteristics of the transmitting and receiving parts. The impact of the flash supply voltage change on the number and energy of the emitted laser pulses is analyzed. The influence of temperature on the generation of the laser beam is also analyzed. The module of the normalized transfer function of the receiver was experimentally determined, and the equivalent bandwidth of the receiver calculated. For the given detection probability of the useful signal and the probability of the number of occurrences of the false signal the required minimum signal/noise ratio of the receiver was calculated. The signal/noise ratio of the receiver for different meteorological visibility conditions was calculated by means of numerical simulation. The influence of the atmosphere on the propagation of the emitted laser radiation was also analyzed. The performance of the analyzed laser range finder fully meets the set standards. Full exploitation of the obtained results is possible on the battlefield, if weather conditions are permanently monitored and taken into account at the time of application in particular area. At the same time, it is possible to actively jam the adversary by using artificial fumes creating devices.

This study proposes a method for detection of uncut crop edges using multiple sensors to provide accurate data for the autonomous guidance systems of head-feeding combine harvesters widely used in the paddy fields of Japan for harvesting rice. The proposed method utilizes navigation sensors, such as a real-time kinematic global positioning system (RTK-GPS), GPS compass, and laser range finder (LRF), to generate a three-dimensional map of the terrain to be harvested at a processing speed of 35 ms and obtain the crop height. Furthermore, it can simultaneously detect the uncut crop edges by RANdom SAmple Consensus (RANSAC). The average of the lateral offset value and crop height of the uncut crop edge detected by the proposed method were 0.154 m and 0.537 m, respectively.

This paper describes a new method of measuring the position of everyday objects and a robot on the floor using distance and reflectance acquired by laser range finder (LRF). The information obtained by this method is important for a service robot working in a human daily life environment. Our method uses only one LRF together with a mirror installed on the wall. Moreover, since the area of sensing is limited to a LRF scanning plane parallel to the floor and just a few centimeters above the floor, the scanning covers the whole room with minimal invasion of privacy of a resident, and occlusion problem is mitigated by using mirror. We use the reflection intensity and position information obtained from the target surface. Although it is not possible to identify all objects by additionally using reflection values, it would be easier to identify unknown objects if we can eliminate easily identifiable objects by reflectance. In addition, we propose a method for measuring the robot’s pose using the tag which has the encoded reflection pattern optically identified by the LRF. Our experimental results validate the effectiveness of the proposed method.

The article describes a new auto-charging system designed to enhance successful docking rates for mobile robots while at the same time monitoring the status of the charging current between the mobile robot and the docking station. The communication interface of the mobile robot and the docking station uses a wireless RF interface. The docking station is designed with one active degree of freedom and two passive degrees of freedom. Users tune the location of the charging pins on the docking station to make it possible for the mobile robot to execute an auto-recharging process. The auto-recharging process uses multiple sensors and a laser range finder located on the mobile robot. The laser range finder searches for a landmark to guide the mobile robot towards the docking station. In the experiment, the power of the mobile robot is under its threshold value. The mobile robot transmits a charging command to the docking station via a wireless RF interface. The docking station transmits a location command back to the mobile robot via this wireless RF interface. The mobile robot uses its laser range finder to search for the landmark of the assigned docking station and programs a motion trajectory to move forward to the docking station. The docking station supplies a charging current to the mobile robot by means of a charger. The power detection module simultaneously detects the charging current and the voltage values of the charging process in both the docking station and the mobile robot. The system can monitor the status of the charging process between the docking station and the mobile robot at any time. The power of the mobile robot is enough to be detected by the power detection module. When the charging process is complete, the docking station turns off the charging current and triggers the mobile robot to leave the docking station via the wireless RF interface.

An autonomous navigation algorithm based on a Particle Filter (PF) was developed.The robot uses only a Laser Range finder sensing the environment.A new probabilistic LIDAR model is developed to be used as measurement model in PF.Robustness demonstrated with field tests in to different field conditions. Autonomous navigation of robots in an agricultural environment is a difficult task due to the inherent uncertainty in the environment. Many existing agricultural robots use computer vision and other sensors to supplement Global Positioning System (GPS) data when navigating. Vision based methods are sensitive to ambient lighting conditions. This is a major disadvantage in an outdoor environment. The current study presents a novel probabilistic sensor model for a 2D range finder (LIDAR) from first principles. Using this sensor model, a particle filter based navigation algorithm (PF) for autonomous navigation in a maize field was developed. The algorithm was tested in various field conditions with varying plant sizes, different row patterns and at several scanning frequencies. Results showed that the Root Mean Squared Error of the robot heading and lateral deviation were equal to 2.4 degrees and 0.04 m, respectively. It was concluded that the performance of the proposed navigation method is robust in a semi-structured agricultural environment.

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.

Curb detection is an important research topic in environment perception, which is an essential part of unmanned ground vehicle (UGV) operations. In this paper, a new curb detection method using a 2D laser range finder in a semi-structured environment is presented. In the proposed method, firstly, a local Digital Elevation Map (DEM) is built using 2D sequential laser rangefinder data and vehicle state data in a dynamic environment and a probabilistic moving object deletion approach is proposed to cope with the effect of moving objects. Secondly, the curb candidate points are extracted based on the moving direction of the vehicle in the local DEM. Finally, the straight and curved curbs are detected by the Hough transform and the multi-model RANSAC algorithm, respectively. The proposed method can detect the curbs robustly in both static and typical dynamic environments. The proposed method has been verified in real vehicle experiments.

Part 4: Optimization, Tuning; International audience; The lidar is nowadays increasingly used in many robotic applications. Nevertheless the 3D lidars are still very expensive and their use on small robots is not economical. This article briefly introduces a construction of cheap 3D lidar for indoor usage based on the 2D laser range finder. Subsequently, this article introduces process of merging acquired point clouds. The every pair of neighboring point clouds is oriented in space to fit together in the best possible way. The result of this process is a 3D map of the robot working environment. This map can be segmented and further used for a navigation. In this way, it is also possible to map inaccessible and dangerous areas.

In this paper, we investigate the use of a laser range finder on a robotic platform for buried water pipe inspection. A robotic platform carrying and manipulating multiple nondestructive inspection sensors may require accurately locating robot's body in the pipe. The laser range finder provides an accurate distance measurement, which can generate a profile of the pipe inner surface. This profile, on one hand, can be used to identify the location of the laser source and thus the robot's body. Such information can further help the navigation of the robot. On the other hand, the anomalies presented in the profile can be detected and characterized in terms of the range measurement. The simulated and real data tests presented in this paper demonstrate the feasibility and effectiveness of incorporating the laser range finder into a robotic platform for the underground pipe inspection.

Self-localization of a robot in an indoor plant is one of the most important requirement in mobile robotics. This paper addresses the application and improvement of a well known localization algorithm used in Robocup Midsize league competition in real service and industrial robots. This new robust approach is based on modeling the quality of several measures and minimizing the maching error. The presented innovative work applies the robotic football knowledge to other fields with high accuracy. Real and simulated results allow to validate the proposed methodology. info:eu-repo/semantics/publishedVersion

This paper proposes a method for mobile robot localization in a partially unknown indoor environment. The method fuses two types of range measurements: the range from the robot to the beacons measured by ultrasonic sensors and the range from the robot to the walls surrounding the robot measured by a laser range finder (LRF). For the fusion, the unscented Kalman filter (UKF) is utilized. Because finding the Jacobian matrix is not feasible for range measurement using an LRF, UKF has an advantage in this situation over the extended KF. The locations of the beacons and range data from the beacons are available, whereas the correspondence of the range data to the beacon is not given. Therefore, the proposed method also deals with the problem of data association to determine which beacon corresponds to the given range data. The proposed approach is evaluated using different sets of design parameter values and is compared with the method that uses only an LRF or ultrasonic beacons. Comparative analysis shows that even though ultrasonic beacons are sparsely populated, have a large error and have a slow update rate, they improve the localization performance when fused with the LRF measurement. In addition, proper adjustment of the UKF design parameters is crucial for full utilization of the UKF approach for sensor fusion. This study contributes to the derivation of a UKF-based design methodology to fuse two exteroceptive measurements that are complementary to each other in localization.

In order to search and rescue victims in rubble effectively, a three-dimensional (3D) map of the rubble is required. As a part of the national project on rescue robot systems, we are investigating a method for constructing a 3D map of rubble by teleoperated mobile robots. In this paper, we developed a laser range finder for 3D map building in rubble. The developed range finder consists of a ring laser beam module and an omnivison camera. The ring laser beam is generated by using a conical mirror and it is radiated toward the interior wall of the rubble around a mobile robot on which the laser range finder is mounted. The ominivison camera with a hyperbolic mirror can capture the reflected image of the ring laser on the rubble. Based on the triangulation principle, cross-section range data is obtained. Continuing this measurement as the mobile robot moves inside the rubble, a 3D map is obtained. We constructed a geometric model of the laser range finder for error analysis and obtained an optimal dimension ...

Range-imaging devices are useful in various applications, both on the ground and in space. In this paper a prototype imaging pulsed time-of-flight laser range finder is presented. The range finder produces an image with 36 pixels, each containing distance and reflectivity information. The scannerless structure is robust and provides instantaneous snapshot-type imaging. An integrated video camera provides high-resolution grey-level images.

This paper describes a multisensor data-fusion system for driving an autonomous earthwork vehicle operating in a sanitary landfill. The system acquires data from a set of ultrasonic sensors, a laser range finder, and several charge-coupled device cameras, and produces, as output, alarms that indicate potentially dangerous situations, e.g., the presence of fixed or mobile obstacles in the vehicle working area. The proposed system adds to the vehicle important functionalities such as avoiding terrain holes or down slopes or discriminating between heaps of waste to be compacted and other man-made obstacles. Data fusion allows the system's reliability to be increased and compensation to be made for the inaccuracies and limited operating ranges of individual sensors. Experimental results shows how the system functions both under normal operating conditions and in the presence of dangerous situations. Moreover, the performance of the system in bad environmental situations (e.g., rain, low lighting) have been evaluated.

Unmanned aerial vehicles represent an effective technology for indoor search and rescue operations. Typically, most indoor missions’ environments would be unknown, unstructured, and/or dynamic. Navigation of UAVs in such environments is addressed by simultaneous localization and mapping approach using either local or global approaches. Both approaches suffer from accumulated errors and high processing time due to the iterative nature of the scan matching method. Moreover, point-to-point scan matching is prone to outlier association processes. This paper proposes a low-cost novel method for 2D real-time scan matching based on a reference key frame (RKF). RKF is a hybrid scan matching technique comprised of feature-to-feature and point-to-point approaches. This algorithm aims at mitigating errors accumulation using the key frame technique, which is inspired from video streaming broadcast process. The algorithm depends on the iterative closest point algorithm during the lack of linear features which is typically exhibited in unstructured environments. The algorithm switches back to the RKF once linear features are detected. To validate and evaluate the algorithm, the mapping performance and time consumption are compared with various algorithms in static and dynamic environments. The performance of the algorithm exhibits promising navigational, mapping results and very short computational time, that indicates the potential use of the new algorithm with real-time systems.

Service robots are not only expected to navigate within the environment, as they also will may with people. Human tracking by mobile robots is essential for service robots and human interaction applications. In this work, the goal is to add a more natural robot–human following in front based on the normal human gait model. This approach proposes implementing and evaluating a human–robot interaction strategy, using the integration of a LRF (Laser Range Finder) tracking of human legs with wearable IMU (Inertial Measurement Unit) sensors for capturing the human movement during the gait. The work was carried out in four stages: first, the definition of the model of human–robot interaction and the control proposal were developed. Second, the parameters based on the human gait were estimated. Third, the robot and sensor integration setup are also proposed. Finally, the description of the algorithm for parameters detection is presented. In the experimental study, despite of the continuous oscillation during the walking, the parameters estimation was precise and unbiased, showing also repeatability with human linear velocities changes. The controller was evaluated with an eight-shaped curve, showing the stability of the controller even with sharp changes in the human path during real experiments. Fil: Cifuentes, Carlos. Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; Brasil Fil: Freire Bastos, Teodiano. Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; Brasil Fil: Carelli Albarracin, Ricardo Oscar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico San Juan. Instituto de Automática; Argentina Fil: Frizera Neto, Anselmo. Universidade Federal Do Espirito Santo. Centro Tecnologico. Departamento de Ingenieria Electrica; Brasil

This paper presents a human detection system that can be employed on board a mobile platform for use in autonomous surveillance of large outdoor infrastructures. The prediction is based on the fusion of two detection modules, one for the laser and another for the vision data. In the laser module, a novel feature set that better encapsulates variations due to noise, distance and human pose is proposed. This enhances the generalization of the system, while at the same time, increasing the outdoor performance in comparison with current methods. The vision module uses the combination of the histogram of oriented gradients descriptor and the linear support vector machine classifier. Current approaches use a fixed-size projection to define regions of interest on the image data using the range information from the laser range finder. When applied to small size unmanned ground vehicles, these techniques suffer from misalignment, due to platform vibrations and terrain irregularities. This is effectively addressed in this work by using a novel adaptive projection technique, which is based on a probabilistic formulation of the classifier performance. Finally, a probability calibration step is introduced in order to optimally fuse the information from both modules. Experiments in real world environments demonstrate the robustness of the proposed method., This work was supported by the Robotics and Cybernetics Group at Universidad Politécnica de Madrid (Spain) and funded under the projects: ROTOS—multi-robot system for outdoor infrastructures protection—sponsored by Spanish Ministry of Education and Science (DPI2010-17998), and ROBOCITY 2030, sponsored by the Community of Madrid (S-0505/DPI/000235/ROBOCITY 2030).

International audience; We demonstrate a laser ranging scheme that uses a high-frequency modulated beam to achieve subnanometer precision by the combined use of interferometric and time-of-flight measurements. We first describe how the absolute distance is extracted from a two-mode interference signal. In particular, we show that the signal, which presents both optical and synthetic wavelength scales, allows one to achieve nanometer-scale accuracy, despite the significant long-term phase drifts in the 20 GHz detection chains. We present results obtained with the telemeter implemented for a distance of about four meters, obtained by folding the laser beam path to the target.

The article of record as published may be found at http://dx.doi.org/10.1109/URAI.2012.6463077 Published in: 2012 9th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI) In this paper, we address the problem of estimating rigid body transformations in two-dimensional space. A new probabilistic weight function is proposed to find a solution closes to odometry information more preferably. The weight function is motivated by hypothesis that a solution of a scan matching problem which prefers odometry information may show better performance in terms of accuracy and convergence. Through the test with three real data sets, our hypothesis was confirmed.

This paper describes a novel registration approach that is based on a combination of visual and 3D range information.To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laserscanner. The matched depth-interpolated image features allows to apply registration with known correspondences.We compare several ICP variants in this paper and suggest an extension that considers the spatial distance betweenmatching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers allscan poses simultaneously., Selected papers from the 2007 European Conference on Mobile Robots (ECMR ’07)

We improve the accuracy of distance measurements with synthetic-wavelength interferometry by referencing the spectral spacing of the free-running light sources to a high-precision radio-frequency oscillator. In addition, we increase the unambiguity range with a time-of-flight technique. Distances to scattering technical surfaces can be measured with micrometer accuracy and an unambiguity range of 1.17 m. The measurement rate amounts to 300 Hz.

小惑星探査機「はやぶさ(MUSES-C: Asteroid Sample-return Spacecraft)」は、2003年5月、ISAS/JAXAにより打ち上げられた。はやぶさのミッションは、サンプルリターンに必要な各工学技術の実証に加え、世界初となる小惑星からのサンプルリターンを行うことである。イオンエンジンを用いた2年以上の航行フェーズの後、2005年9月12日に、はやぶさは小惑星イトカワ(Asteroid 25143)に到着した。小惑星近傍では、小惑星の詳細な観測に成功し、サンプル採取のための着地2回を含む計5 回の降下が行われた。地球への帰還は2010年の予定である。本論文では、1回目に行われた小惑星着地時における、はやぶさの軌道・姿勢について扱う。降下初期、小惑星からの高度250mまでは、小惑星画像を用いた誘導航法が行われ、その後は自立航法により着地したが、後半の軌道をもとめることは困難であった。小惑星画像やセンサデータが十分に得られていないためである。しかし、1回目の降下においては、断片的ではあるが1部のセンサデータが得られているため、小惑星形状モデルや小惑星上に投下されたターゲットマーカの画像を組み合わせて軌道・姿勢を推定する。, On May 9, 2003, the Institute of Space and Astronautical Science (ISAS/JAXA) launched an asteroid sample return spacecraft called 'Hayabusa (MUSES-C: Asteroid Sample-return Spacecraft)' (named after falcon). Hayabusa aims for the world's first sample return from an asteroid along with demonstrations of several space engineering technologies for sample-return. After more than two years' cruising phase by its ion engines, it arrived at Itokawa (Asteroid 25143) on September 12, 2005. In the vicinity of the asteroid, Hayabusa succeeded in detail asteroid observations and performed five times' descent flights including twice landing operation on Itokawa to obtain asteroid samples. Its Earth return is expected around 2010. In this research, the Hayabusa trajectory and attitude during its touchdowns are discussed in detail. In the initial descent operation, above the altitude of 250 m, the guidance and navigation was conducted by determining the position using Itokawa images. After that, Hayabusa descended to Itokawa autonomously. The trajectory at this phase has not been revealed yet. It is because Itokawa images and navigation sensor data were not fully obtained. However, as to the first landing, several kinds of useful sensor data exist, though they are fragmentary. The trajectory and attitude of the final descent phase during the first landing operation was estimated by combining information from these sensor data. The Itokawa 3D shape model and the T/M (Target Marker) image on the Itokawa ground surface were also used., 資料番号: AA0063481024

Absolute distance measurements can be performed with an interferometric method that uses only a single tunable laser. This method has one major drawback, because a small target movement of the order of one wavelength during a measurement will be interpreted as a movement of one synthetic wavelength. This effect is usually mitigated by adding a second (nonscanning) laser. We show that absolute distance measurements can be performed with only one laser if the movements encountered are smooth, on the time scale of one measurement. In this case the movement errors can be compensated with a simple algorithm that combines several subsequent measurements. First experimental results show good agreement with theory.

This paper analyzes the Kinect limitationa and proposes a system setup equipped with both Kinect and laser range finder.

We present an optimization technique utilizing order statistics with a multichannel digital silicon photomultiplier (MD-SiPM) for timing measurements. Accurate timing measurements are required by 3D rangefinding and time-of-flight positron emission tomography, to name a few applications. We have demonstrated the ability of the MD-SiPM to detect multiple photons, and we verified the advantage of detecting multiple photons assuming incoming photons follow a Gaussian distribution. We have also shown the advantage of utilizing multiple timestamps for estimating time-of-arrivals more accurately. This estimation technique can be widely available in various applications, which have a certain probability density function of incoming photons, such as a scintillator or a laser source.

This paper reports on a light detection and ranging (LIDAR) system that incorporates a microelectromechanical-system (MEMS) mirror scanner and a single-photon imager. The proposed architecture enables a high signal-to-background ratio due to pixel-level synchronization of the single-photon imager and the MEMS mirror. It also allows the receiving optics to feature a large aperture, yet utilizing a small MEMS device. The MEMS actuator achieves a mechanical scanning amplitude of ±4° horizontally and ±3° vertically, while the field of view of the overall sensor is 45 by 110. Distance images were acquired outdoors in order to qualitatively evaluate our sensor imaging capabilities. Quantitative ranging performance characterization carried out under 10 klx of ambient light revealed a precision of 14.5 cm throughout the distance range to 25 m, thus leading to a relative precision of 0.58%.

In this paper, a novel extrinsic calibration method of a camera and a laser range finder is presented using a calibration cube. The calibration is based on observing three edges of the cube from the camera and three laser points projected onto the edges from the laser finder. The equation for extrinsic calibration parameters, a rotation matrix and a translation vector, is built based on the constraint that in the image coordinate the projection of laser point onto the image plane is on that of the three cube edges. The parameters are calculated with three changes of pose of the cube. Experimental result shows that the proposed method is feasible and accurate.

Background Avoiding a fall requires fast and appropriate step responses, stepping speed as a fall risk indicator has only been assessed in older adults. We have developed a new measurement system that applies a laser range finder to assess temporal and spatial parameters of stepping performance such as step speed, length, and accuracy. This measurement system has higher portability, lower cost, and can analyze a larger number of temporal and spatial parameters than existing measurement systems. The aim of this study was to quantify the system for measuring reaction time and stride duration by compared to that obtained using a force platform. Methods Ten healthy young adults performed steps in response to visual cues. The measurement system applied a laser range finder to measure the position and velocity of the center of each leg and of both legs. We applied the developed measurement system to the rhythmic stepping exercise and measured reaction time and stride duration. In addition, the foot-off time and foot-contact time were quantified using the measurement system, and compared to the foot-off time and foot-contact time quantified using a force platform. Results We confirmed that the measurement system can detect where a participant stood and measured reaction time and stride duration. Remarkable consistency was observed in the test-retest reliability of the foot-off time and foot-contact time quantified by the measurement system (p r = 0.997, stride duration: r = 0.879; p Conclusions The new measurement system provided a valid measure of temporal step parameters in young healthy adults. The validity of the system to measure reaction time and stride duration was evaluated, and confirmed by applying to the rhythmic stepping exercise.