Mobile Robots In Outdoor Environments Research Articles

In situ skid estimation for mobile robots in outdoor environments

AbstractSkidding is a surface hazard for mobile robots' navigation and traction control systems when operating in outdoor environments and uneven terrains due to the wheel–terrain interaction. It could lead to large trajectory tracking errors, losing the robot's controllability, and mission failure occurring. Despite research in this field, the development of a real‐world feasible in situ skid estimation system with the capability of operating in harsh and unforeseen environments using low‐cost/power and ease of integrating sensors is still an open problem in terramechanics. This paper presents a novel velocity‐based definition for skidding that enables a real‐world feasible estimation for the mobile robot's undesired skidding at the vehicle‐level in outdoor environments. The proposed technique estimates the undesired skidding using a combination of two proprioceptive sensors (e.g., inertial measurement unit and wheel encoder) and deep learning. The practicality of a velocity‐based definition and the performance of the proposed undesired skid estimation technique are evaluated experimentally in various outdoor terrains. The results show that the proposed technique performs with less than 11.79 mm/s mean absolute error and estimates the direction of undesired skidding with approximately 98% accuracy.

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

Motion Tracking of Four-Wheeled Mobile Robots in Outdoor Environments Using Bayes’ Filters

In situ slip estimation for mobile robots in outdoor environments

AbstractAccounting for wheel–terrain interaction is crucial for navigation and traction control of mobile robots in outdoor environments and rough terrains. Wheel slip is one of the surface hazards that needs to be detected to mitigate against the risk of losing the robot's controllability or mission failure occurring. The open problems in the Terramechanics field addressed are (1) the need for in situ wheel‐slippage estimation in harsh environments using low‐cost/power and easy to integrate sensors, and (2) removing the need for prior information of the soil, which is not always available. This paper presents a novel slip estimation method that utilizes only two proprioceptive sensors (IMU and wheel encoder) to estimate the wheel slip using deep learning methods. It is experimentally shown to be real‐world feasible in outdoor, uneven terrains without prior soil information assumptions. Comparison with previously used machine learning algorithms for continuous and discrete slip estimation problems show more than 9% and 14% improvement in estimation performance, respectively.

A Novel Lidar-Assisted Monocular Visual SLAM Framework for Mobile Robots in Outdoor Environments

In this paper, a novel 3D lidar-assisted monocular visual SLAM (LAMV-SLAM) framework is proposed for mobile robots in outdoor environments. LAMV-SLAM can run in real-time without a GPU and build a dense map with real scale. An online photometric calibration thread is integrated into LAMV-SLAM to eliminate the photometric disturbances in images. The tracking thread combines the lidar and vision data to estimate and refine the frame-to-frame transformation. In this thread, the depth fusion algorithm is proposed to provide accurate depth values for the extracted visual features by combining the lidar points, and a novel two-stage optimization method is proposed to utilize the fused lidar-vision data to estimate the camera transformation with the real scale. A parallel mapping thread generates new map points based on depth-filter and lidar-vision data fusion. A loop closing thread further reduces the accumulative errors of the system. To verify the accuracy and efficiency of the system, we evaluated the proposed pipeline on the KITTI odometry benchmark, and our LAMV-SLAM achieves a 0.81% of relative position drift while running at over 3X real-time speed. To verify the robustness of the system in challenging environments, experiments were carried out on the NCLT and NuScenes datasets. Moreover, real-world experiments were applied to our mobile robot platform to show the practicability and validity of the proposed approach.

Curve-Localizability-SVM Active Localization Research for Mobile Robots in Outdoor Environments

Working environment of mobile robots has gradually expanded from indoor structured scenes to outdoor scenes such as wild areas in recent years. The expansion of application scene, change of sensors and the diversity of working tasks bring greater challenges and higher demands to active localization for mobile robots. The efficiency and stability of traditional localization strategies in wild environments are significantly reduced. On the basis of considering features of the environment and the robot motion curved surface, this paper proposes a curve-localizability-SVM active localization algorithm. Firstly, we present a curve-localizability-index based on 3D observation model, and then based on this index, a curve-localizability-SVM path planning strategy and an improved active localization method are proposed. Obtained by setting the constraint space and objective function of the planning algorithm, where curve-localizability is the main constraint, the path helps improve the convergence speed and stability in complex environments of the active localization algorithm. Helped by SVM, the path is smoother and safer for large robots. The algorithm was tested by comparative experiments and analysis in real environment and robot platform, which verified the improvement of efficiency and stability of the new strategy.

Accurate real-time visual SLAM combining building models and GPS for mobile robot

This paper presents a novel 7 DOF (i.e., orientation, translation, and scale) visual simultaneous localization and mapping (vSLAM) system for mobile robots in outdoor environments. In the front end of this vSLAM system, a fast initialization method is designed for different vSLAM backbones, which upgrades the accuracy of trajectory and reconstruction of vSLAM with an absolute scale computed from depth maps generated by building blocks. In the back end of this vSLAM, we propose a nonlinear optimization mechanism throughout which multimodal data are combined for more robust optimization. The modality of building blocks in optimization can improve the tracking accuracy and the scale estimation. By integrating the pose estimated from visual information and the position received through GPS, the optimization further alleviates the drift. The experimental results prove that the proposed method is extremely suitable for outer AR application for outdoor environments, because our method has superior initialization performance, runs in real time, and achieves real scale, higher accuracy, and robustness.

Caracterización de un Sistema GPS RTK de Bajo Costo

En las últimas décadas la comunidad robótica ha adoptado cada vez más el uso de sensores GPS-RTK para estimar de forma precisa la localización de robots móviles en ambientes exteriores. En este trabajo se presenta la caracterización de un sistema GPS-RTK de bajo costo. A partir del montaje de las unidades base y móvil se adquirieron datos de posición corregidos en tiempo real por el mismo sistema RTK bajo diferentes disposiciones físicas, velocidades de actualización y condiciones ambientales presentándose posteriormente un estudio estadístico de los resultados obtenidos.

Sensor-Fusion Based Navigation for Mobile Robot in Outdoor Environment

Autonomous navigation of the vehicles or robots is very challenging and useful task used by many scientists and researchers these days. By keeping this fact in mind, an algorithm for autonomous navigation of mobile robot in outdoor environment is proposed. This navigation track consists of colored border containing obstacles and some unplanned surfaces along with some specific points for GPS (Global Positioning System) alarms. The main goal is to avoid colored border and obstacles. For the vision problem webcam is used. First the colored border is detected by using OpenCV library by following HSI (Hue Saturation Intensity)technique. The Canny edge algorithm is used to find both edges of the border, then for detecting straight lines on both sides of track, Hough transformation is used. Finally, the closest border line is detected and its center point is calculated for which the mobile robot has to steer to avoid it. Second step is to avoid the obstacles, which is done by LRF (Laser Range Finder), first the range of LRF is defined, because not all obstacles have to be avoided, only those obstacles are detected and avoided which are in specified range as defined before, and finally GPS receiver is used to make alarms at some specific points. As a result, a successful navigation of the mobile robot in the outdoor environment is implemented.

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

Here we report on a new method for robotic gas source localization. Research efforts are being made to develop mobile robots that can autonomously search for gas sources. The tasks envisaged for such robots include detecting gas leaks in industrial plants and exploring gas emissions in landfill sites. If a mobile robot is provided with gas sensors, it can detect the presence of a target gas drifting in the air. However, to find the location of the gas source is not a trivial task for the robot. Gas molecules released in the air are carried by the airflow and form a plume in the downwind direction. Since the airflow the robot encounters in gas source localization applications is almost always turbulent, the plume randomly meanders and comes to have a complicated patchy structure. Gas source localization in outdoor environments is especially challenging owing to the large fluctuations in the wind direction. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. Two different turbulent plume models were compared regarding the accuracy in the estimated gas source location. The results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

Estimation of Gas Source Location from Fluctuating Readings of Gas Sensors and Anemometer on Mobile Robot in Outdoor Environment

Here we report on a new method for robotic gas source localization. In the method proposed in this series of work, a mobile robot equipped with gas sensors and an anemometer is used to measure the gas concentration and airflow direction at various locations in the area under consideration. The location of the gas source is estimated by applying a turbulent diffusion model of a gas plume to the sensor data collected by the mobile robot. In the previous version of the method, the value of the turbulent diffusion coefficient in the model needs to be properly adjusted to obtain appropriate estimation results. Therefore, the model is replaced with the one that requires no empirical parameter. The estimation results are presented to show that the proposed method can provide a map showing the area in which a gas source is likely to be found.

Detection of traversable region around a mobile robot by computing terrain unevenness from the range data of a 3D laser scanner

Purpose – The purpose of this paper is to detect traversable regions surrounding a mobile robot by computing terrain unevenness using the range data obtained from a single 3D scan. Design/methodology/approach – The geometry of acquiring range data from a 3D scan is exploited to probe the terrain and extract traversable regions. Nature of terrain under each scan point is quantified in terms of an unevenness value, which is computed from the difference in range of scan point with respect to its neighbours. Both radial and transverse unevenness values are computed and compared with threshold values at every point to determine if the point belongs to a traversable region or an obstacle. A region growing algorithm spreads like a wavefront to join all traversable points into a traversable region. Findings – This simple method clearly distinguishes ground and obstacle points. The method works well even in presence of terrain slopes or when the robot experiences pitch and roll. Research limitations/implications – The method applies on single 3D scans and not on aggregated point cloud in general. Practical implications – The method has been tested on a mobile robot in outdoor environment in our research centre. Social implications – This method, along with advanced navigation schemes, can reduce human intervention in many mobile robot applications including unmanned ground vehicles. Originality/value – Range difference between scan points has been used earlier for obstacle detection, but no methodology has been developed around this concept. The authors propose a concrete method based on computation of radial and transverse unevenness at every point and detecting obstacle edges using range-dependent threshold values.

2A1-U07 屋外環境での自律移動型ロボットにおけるオクルージョンを考慮した人物追跡

2A1-U07 屋外環境での自律移動型ロボットにおけるオクルージョンを考慮した人物追跡

Robust Visual Robot Localization Across Seasons Using Network Flows

Image-based localization is an important problem in robotics and an integral part of visual mapping and navigation systems. An approach to robustly match images to previously recorded ones must be able to cope with seasonal changes especially when it is supposed to work reliably over long periods of time. In this paper, we present a novel approach to visual localization of mobile robots in outdoor environments, which is able to deal with substantial seasonal changes. We formulate image matching as a minimum cost flow problem in a data association graph to effectively exploit sequence information. This allows us to deal with non-matching image sequences that result from temporal occlusions or from visiting new places. We present extensive experimental evaluations under substantial seasonal changes. Our approach achieves accurate matching across seasons and outperforms existing state-of-the-art methods such as FABMAP2 and SeqSLAM.

Terrain-inclination-based Three-dimensional Localization for Mobile Robots in Outdoor Environments

A new terrain-inclination-based localization technique is proposed in this paper to enable a robot to identify its three-dimensional location relative to measurable terrain inclinations. Given a topographical map and a planned path, a robot-terrain-inclination model RTI model is extracted along the path on the terrain upon which the robot is operating. A particle filter is then used to fuse the measurement data with the robot motion based on the extracted RTI model for either a three-wheeled or a four-wheeled mobile robot. Experiments were carried out in four outdoor scenarios: one short path with different initial conditions and map resolution, another short path with different surface roughness and sensor accuracy, and two long paths with different types of rigid terrains and multiple loops. Experimental results show that the proposed method could achieve good localization performance on inclined outdoor terrains.

3P1-K05 マルチ移動ロボットによるレーザベーストな移動物体大きさ・姿勢推定法(3次元計測/センサフュージョン)

This paper presents a laser-based moving-object tracking system by multiple mobile robots in outdoor environments. For moving-object tracking, each robots identify its own postures using real-time-kinematic(RTK)-GPS. Each of the robots detects moving objects using its own laser scan image by an occupancy-grid-based method. It sends the information of the detected moving objects to the central server through intercommunication. The central server estimates position and velocity of moving objects via Kalman filtering based on received information. And also it estimates size of moving objects, and their estimates are fed back to the robots. Two experimental results show the performance of our tracking system.

Autonomous Pedestrian Push Button Activation by Outdoor Mobile Robot in Outdoor Environments

The authors have developed an outdoor mobile robot that has the ability to cross roads at an intersection or pedestrian crossing fully autonomously while traveling along sidewalks in an urban environment. This gives the robot the capability to travel longer and complex routes as the robot is able to cross a road and continue with its path. The developed robot has the unique ability to autonomously approach and activate the pedestrian push button with a mechanical finger. We first briefly describe the overall operation of such a road crossing robot. The rest of this paper then discusses in detail how the robot can meticulously navigate to and activate the pedestrian push button with the on-board finger. The contribution of this work is that although there are robots existing that perform precision docking or button activation, this robot is one of the few that can perform such an action in a real world outdoor environment that is completely unmodified. We prove this by deploying the robot in a real world road-crossing and it was successfully able to engage the pedestrian push button.

A Set-Theoretic Algorithm for Real-Time Terrain Mapping of Mobile Robots in Outdoor Environments

In this paper, we address the problem of mapping outdoor rough terrain environments for mobile robots. While uncertainties arising from multiple sources are considered explicitly and assumed to be unknown-but-bounded, a set-theoretic framework is proposed to construct the terrain model as a set-valued elevation map that extends the notion of the elevation map with elevation variation in each cell stored by intervals. The localization problem of the mobile robot is also considered and solved by a set-membership filter in order to provide guaranteed bounded-pose estimation, which can be incorporated to the elevation map to improve the accuracy of the final terrain model. A more compact terrain representation can be obtained by the proposed algorithm with relatively low computational complexity, which makes it suitable for real-time applications. Furthermore, improved smoothness is achieved by the inherent conservativeness of the set-theoretic method without additional filtering or interpolation processes. Simulations as well as real-life experiments of a mobile robot operating in outdoor rough terrain environments with a 2D scanning laser rangefinder demonstrate the effectiveness and robustness of the proposed method.

GPS 정보와 차선정보의 정합을 통한 이동로봇의 실외 위치추정

Accurate localization is very important to stable navigation of a mobile robot. This paper deals with local localization of a mobile robot especially for outdoor environments. The GPS information is the easiest way to obtain the outdoor position information. However, the GPS accuracy can be severely affected by environmental conditions. To deal with this problem, the GPS and wheel odometry can be combined using an EKF (Extended Kalman Filter). However, this is not enough for safe navigation of a mobile robot in outdoor environments. This paper proposes a novel method using lane features from the road image. The pose data of a mobile robot can be corrected by analyzing the detected lane features. This can improve the accuracy of the localization process substantially.

Real-time 3D Outdoor Environment Modeling for Mobile Robot with a Laser Scanner

The real-time terrain modeling problem of mobile robot with a laser scanner in outdoor unstructured 3D environments is studied.The underlying uncertainties from multiple sources during modeling are taken into account and modeled as zero-mean Gaussian noises,and subsequently the multi-level coordinate transformation matrixes are created to convert the measurements from laser scanner into probabilistic elevation estimations in the global coordinate systems,which will be associated with several terrain cells according to the confidence interval of the estimation.The elevation estimations assigned to each cell can be fused through a probabilistic approach to update the map locally.In addition,a local measurement window is defined to detect the occlusions,and the 3D localization of the mobile robot in outdoor environment is solved simultaneously. Experimental results demonstrate the real-time performance and effectiveness of the proposed method.

Online Modelling and Tracking Control of Mobile Robots with Slippage in Outdoor Environments

The problems of slippage modeling and compensating control for mobile robots in outdoor environments are studied.Focusing on a tracked mobile robot,the slippage efficiency between tracks of a tracked mobile robot and ground is analyzed and modeled as time-varying parameters,meanwhile the corresponding kinematic and dynamic models of the robot are created with slipping parameters.Then the online nonlinear estimators such as the square-rooted unscented Kalman filter (UKF)can be used to estimate the pose and slipping parameters jointly.Furthermore the dynamic feedback linearization integrated with a globally exponentially convergent control law is applied to the tracking control.Simulation results demonstrate the effectiveness and robustness of the proposed method.