Human Following for Outdoor Mobile Robots Based on Point‐Cloud's Appearance Model

Chinese Journal of ElectronicsVolume 30, Issue 6 p. 1087-1095 Original Research PaperFree Access Human Following for Outdoor Mobile Robots Based on Point-Cloud's Appearance Model GONG Linxi, Corresponding Author GONG Linxi glx0118@163.com School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this authorCAI Yunfei, Corresponding Author CAI Yunfei cyf@njust.edu.cn School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this author GONG Linxi, Corresponding Author GONG Linxi glx0118@163.com School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this authorCAI Yunfei, Corresponding Author CAI Yunfei cyf@njust.edu.cn School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094 ChinaSearch for more papers by this author First published: 01 November 2021 https://doi.org/10.1049/cje.2021.07.017 This work is supported by the Fund for Technical Field of the Foundation Strengthening Programme (No.2019-JCJQ-JJ-355). AboutSectionsPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract In this paper, we propose a point-cloud-based algorithm for human-following robots to detect and follow the target person in a complex outdoor environment. Specifically, we exploit AdaBoost to train a binary classifier in a designed feature space based on sparse point-cloud to distinguish the target person from other objects. Then a particle filter is applied to continuously track the target's position. Motivated by the interference of obstacles in long-distance human-following scenarios, a motion plan algorithm based on vector field histogram is adopted. Experiments are carried out both on the dataset we collected and in real application scenarios. The results show that our algorithm has the ability of real-time target detection and tracking, and is robust to deal with complex situations in outdoor environments. I. Introduction As an important task of human-robot interaction, the autonomous human-following behavior of mobile robots can be widely applied to task scenarios that require human-robot collaboration and has been an active research field. In recent years, the research orientation is mainly focused on small indoor mobile robots in short-distance following scenarios like assistant robots in hotels or hospitals[1]. Meanwhile, with regards to complex outdoor environments, the problem is obviously more complicated due to the possible large scale change in perception data because of bumpiness. Overall, a robust human-following algorithm has two essential issues to be addressed: 1) a robust detection and tracking algorithm that helps identify and locate the target person and 2) a motion controller to generate and follow the path depending on the target's position. Target detection and tracking are the premises of autonomous human-following tasks. The target person is assumed to always be leading in front of the robot. However, the position of the target can vary widely in real application scenarios. It is essential that the robot can detect the target person within its perception range. In addition, unlike common tracking algorithms, robot's following behavior also requires a time-varying position of the target person relative to the robot. Therefore, Laser range finder is a common sensor choice for human-following robots because it can provide distance and direction information of the target. To extract a unique feature of the target person in a LiDAR (LightLaser detection and ranging) scan faces many difficulties. In 2D LiDAR scans, leg-patterns are often designed and extracted. While in 3D LiDAR scans, effective descriptors are often computational expansive and therefore not suitable for real-time applications[2]. Concerning that the natural behavior of the leading person could involve any sudden turns, the feature descriptor should be irrelevant of any orientations. The distance and direction of the target person relative to the robot should also be taken into consideration for spatiotemporal continuity in the robot's behavior. In this paper, we consider an orientation irrelevant feature in a sparse point-cloud with respect to the appearance and relative position of the target person. For the reason that a single target detection problem can be regarded as a binary classification problem between the target human and other objects, the feature is then put through a pre-trained AdaBoost classifier to distinguish the target person from other objects. A particle filter is adopted to predict and update the target's position to continuously track the target in the event of a short loss of personnel. In most application scenarios of human-following tasks, robots without prior knowledge of the environment generate and track their paths autonomously. We consider direction-oriented following strategy for the reason that it is more natural than robots following a human's trajectory[3]. It is also worth mentioning that due to the tracked vehicle used in our application and its possible massive volume, safeness should also be taken into great consideration. Long-distance human-following experiments are carried out in this article to ensure safeness of the robots following behavior. Considering the possible interference of obstacles in long-distance human-following scenarios, an obstacle avoidance method based on vector field histogram is applied. The rest of this article is unfolded in the order as follows. First, relevant work of human-following robot systems is described in Section II. Then, the overall system architecture is given in Section III. Section IV gives a full description of the target detection and tracking algorithm we propose. Section V describes the controlling mechanism of robot's following action. The experimental results will be given in Section VI. In Section VII, a conclusion is drawn with future work prospects. II. Related Work Over the years, many researchers have given complete solutions for autonomous human-following tasks. The purposes of these researches are mostly to assist or accompany a single person in a dynamic multi-agent environment[1]. Sensor choices are made among vision camera, depth camera and laser range finder, while some of them apply general sensor fusion methods for perception. Many early versions of human-following robots are based on visual texture of the target[4]. Isobe et al.[5] extracts hue and saturation information of each person detected to be compared with the target registered previously. This kind of method is less effective in case of out-of-plane rotation, because the visual texture will change significantly at such times. Gupta et al.[6] dealt with out-of-plane rotation by applying affine transformation on target template, keeping target template updated during tracking. Optical flow is a relatively mature method in visual object tracking[7] therefore often used in human-following tasks. An online convolutional network is adopted in the work of Chen et al.[8] to track the target person in RGBD camera frames. In these works, the control signal is either calculated based on the target's position measured by the depth camera, or generated directly based on the visual control strategy[6]. Some researchers extract leg-patterns from 2D LiDAR scans to detect human[9, 10–11]. Such LiDARs are generally mounted at the height of the calf of a person. Cosgun et al.[9] choose to track a single leg by calculating Mahalanobis distance between two segments in associated frames. Cha et al.[10] used support vector data description to project the 2D information to a 3D feature space for leg detection. However, 2D LiDAR has a narrow field of vertical view which have limited its use in outdoor field environment. In Ref.[12], a hierarchical sample-based joint probabilistic data association filter is proposed and possible locations of multiple human targets can be simultaneously estimated on the basis of Bayesian filtering. As for the motion control, proportional control, PID control and vision control strategies are commonly used methodologies in human following tasks to maintain a certain distance between robot and target person[6]. However, few human-following robots in previous research have taken autonomous obstacle avoidance into consideration. Most researches only considered robot's behavior in indoor, close-range following scenarios. Their requirements for obstacle avoidance can be resolved by the detour taken by the target person, knowing the presence of the robot. Chen et al.[8] consider the control strategy in the case of short-term loss of target. In their research, the method to retrieve the target is based on the robot's estimation of its global position. Yuan et al.[11] design a potential-field-based human tracker by integrating the intersection information to estimate the target's position in case of short-term target loss. From existing researches, it is observed that the research orientation of human-following robots is mostly focused on small robots in close-range following tasks and the application scenarios are mostly indoor and structured outdoor environments. And the robustness of the system in complex outdoor environments, such as in rough terrain, has seldom been taken into consideration. III. System Framework The human-following architecture in this article consists of three main parts: detection, tracking and controlling. The detection section is based on a newly proposed human's appearance model of sparse point clouds. A target person is detected in point-cloud's feature space through a binary classifier based on Adaboost. The tracking section uses a particle filter to predict and update the target's position. It is set to allow several frames of target loss during the tracking process. The controlling section adopts a path planner based on vector field histogram to avoid obstacles. And a feedback controller is used to maintain a relative position between the robot and the target person. These sections mentioned above are connected in series. The overall schema of the system's framework is shown in Fig.1. IV. Target Detection and Tracking Different from general target detection and tracking algorithms, human-following tasks target person specifically. Base on this prior knowledge and the fact that feature descriptors in higher dimension are computationally expensive and not suitable for real-time tasks, a simple and easy-to-construct feature space is designed. Then, an AdaBoost classifier is applied to address the target-non-target binary classification problem. Before any of these above process, in order to reduce the calculation expense and improve accuracy, the point cloud is preprocessed to have the ground points removed and region of interest intercepted. Then, a point-level density based clustering algorithm is performed to have the point-cloud clustered. Fig. 1Open in figure viewerPowerPoint System framework 1. Point cloud preprocess The point cloud obtained by LiDAR, although reliable and robust, is redundant of data not needed in target detection process, such as ground points or distant points. Preprocess of the point-cloud is needed to enhance the results of clustering as well as reduce the time complexity of the algorithm. We apply a simple pass through filter based on experience to remove ground points. The point cloud is then cut for a Region of interest (ROI), which is centered on the position of the target person's last appearance. A density-based spatial clustering method is applied to have stable point-cloud cluster candidates for feature extraction. The result of clustering is shown in Fig.2. Fig. 2Open in figure viewerPowerPoint Schematic diagram of clustering result. (a) Original point cloud; (b) Clustering result 2. Target detection in feature space based on AdaBoost A single target detection problem can be regarded as a target-non-target binary classification problem. Therefore, we applied a binary classifier based on AdaBoost, an ensemble learning method, to address this problem. The feature space defined in our approach puts emphasis on the target's position and velocity relative to the robot, as well as the characteristic of the point-cloud's appearance and reflection intensity. Specifically, a local map is maintained by the robot during the whole following process and its origin point is set to be the position of Inertial navigation system (INS) mounted at the center of the robot. Note that coordinates of points mentioned below are in the above coordinate system. We assume a set of point-clusters S obtained by the previous process of the point-cloud, S = { C 1 , C 2 , . . . , C n } , where C i is a set of l i points, C i = { p 1 , p 2 , . . . , p l i } . A point p j = { x j , y j , z j } in the set C i , j ∈ { 1 , 2 , . . . , l i } , contains information of its position in robot's local map. x j = d j × cos θ j 2 × sin θ j 1 y j = d j × cos θ j 2 × cos θ j 1 z j = d j × sin θ j 2 (1)where d j is the distance between p j and the sensor. θ j 1 , θ j 2 are horizontal and vertical angles between p j and the standard axis of the local map. 1) Feature definition A function φ is defined as Φ : C → f that takes a point-cloud cluster C i as an argument and returns a 21 dimensional feature f. The feature is determined below: a) Number of points n = ∥ C i ∥ . b) Center of gravity P c . P c = 1 l i ( ∑ j = 1 l i x j , ∑ j = 1 l i y j , ∑ j = 1 l i z j ) (2) c) Standard deviation of cluster points' coordinate. σ = 1 l i - 1 ∑ j = 1 l i ∥ p j - P c ∥ (3) d) Linear velocity. Target's linear velocity v = { v x , v y } at a given time t is assumed same as that of the previous frame. Given two previous frames with associated timestamp S t - 1 , S t - 2 and their observation of target's position given in previous process T t - 1 , T t - 2 , the linear velocity is shown in Eq.(4). v x t = x T t - 1 - x T t - 2 Δ t , v y t = y T t - 1 - y T t - 2 Δ t (4) e) Measurement of sliced cloud cluster. In a sparse point cloud, it is hard to detect unique shapes of human. By measure the horizontally sliced cloud cluster, we can distinguish humans from some other false targets[13], including trees and poles in outdoor environments. As shown in Fig.3, the first engine vector is parallel to the z coordinate axis of the mobile robot, then the other two main engine vectors are computed by Principal component analysis (PCA). Therefore, the definition of this feature is given by Eq.(5). f 5 = { w 00 , w 01 , . . . , w N 0 , w N 1 } (5)where N is the slice number along the main vector direction, and f 5 is a 2 N + 1 dimensions vector. N is taken 6 here considering the sparsity. f) Mean value of reflection intensity I c . g) Standard deviation of reflection intensity σ i . σ i = 1 l - 1 ∑ j ∥ I j - I c ∥ (6) Note that in target following tasks, the robot is supposed to maintain a certain distance with target person in the three-dimensional world, thus robot's position relative to the target is implicitly utilized in feature definition as an important component. Section VI.1 shows that the performance of a feature varies depending on whether there is relative location information. 2) AdaBoost classifier In the scenario of single target detection, the main problem can be transformed to a target-non-target binary classification problem. A reliable binary classifier based on AdaBoost is applied to distinguish target human in the feature space given above. AdaBoost is a method of ensemble learning which strategically combines base learners to get a strong classifier. The base learner is chosen to be a decision tree with one layer. Taking both success rate and computational expanse into consideration, the number of base learners is set to be 50 in practice. Fig. 3Open in figure viewerPowerPoint Slice feature extraction of a human shaped cluster The generalized AdaBoost algorithm is shown in Algorithm 1, where x i is a vector containing d elements, x i ⊆ R d , and y i is a label scalar, y i ∈ { - 1 , + 1 } . 3. Centroid tracking based on particle filter In the human-following scenario, the continuity in the target's trajectory is difficult to be modeled with a linear motion model. Therefore, a standard particle filter is used to track the potential target. The generalized procedure of a basic particle filter is as follows. 1) Sampling based on prior probability density function. z 0 ( i ) ∼ q ( z 0 ) , i = 1 , 2 , . . . , N (7) w 0 ( i ) = p ( z 0 ( i ) ) q ( z 0 ( i ) ) , i = 1 , 2 , . . . , N (8) 2) Sequential importance sampling. Based on the latest observation, the particle weights are updated by the likelihood function calculation. w t ( i ) = w t - 1 ( i ) p ( z t | x t ( i ) ) p ( x t ( i ) | x t - 1 ( i ) ) q ( x t ( i ) ) , i = 1 , 2 , . . . , N (9) w ¯ t ( i ) = w t ( i ) ∑ i = 1 N w t ( i ) , i = 1 , 2 , . . . , N (10) 3) Resampling at equal frequencies to update particles. We give each potential target candidate a filter, and each filter uses 80 particles. For the reason that any complex motion model cannot well portrait human behaviors, such as sudden stops or turns, a prediction is made with the Brownian motion model of where the potential target will be in the next frame. In the event of lost echo of the target, the position is assumed to the same as the previous frame. A loss of observation in 5 frames is allowed for the continuity in position. The overall algorithm flow is shown in Fig.4. Fig. 4Open in figure viewerPowerPoint Flowchart of detection and tracking process V. Motion Plan and Controlling 1. Motion plan based on VFH The motion planning algorithm determines the way robot reaches the target location under various constraints. In order to enable the mobile robot to have a certain degree of obstacle avoidance abilities, so it can go around corners and roadblocks in a relatively long-distance following task, a motion plan algorithm based on vector field histogram is adopted. The specific flow of the algorithm is as follows. 1) Interval division The front 180 ∘ of laser's perception range is divided into N intervals by an interval of 2 . 5 ∘ . Then the intervals are labeled counterclockwise. The i-th interval is represented by Z i = { v 1 , v 2 , . . . , v n } , where v j , j = 1 , 2 , . . . , n , denotes the obstacle located in the i-th interval. In this paper, the point-cloud is processed to have a 2-D occupational grid map based on the height difference of points in corresponding grid. Then the occupied grids are converted into vectors described in polar coordinate system. 2) Selection and merging As shown in Fig.5, the minimum distance of obstacle d min in an interval is obtained by scanning each interval Z i . If d min is greater than the selected threshold T, the interval is classified as a candidate interval. We merge adjacent candidate intervals to form a feasible sector, which can be described as a four-dimensional vector: S = { R j , θ j , n j s , n j e } (11)where R j represents a given passable distance, θ j represents the range of angle between the feasible sector's centerline and the x-axis of the robot's polar coordinate, and n j s , n j e represents the start and end interval numbers of the sector. 3) Feasible sector selection we scan each candidate feasible sectors in parallel direction to expand the path into a passable area and carry out motion planning. We compare θ cur , which is the angle of current selected sector's centerline, with the angle of the target direction θ target to make: arg min θ cur | θ target - θ cur | (12) θ cur is obtained as the final direction. Fig. 5Open in figure viewerPowerPoint Illustration of vector field histogram method. (a) Diagram of overall method in a grip map; (b) Flowchart of motion plan based on VFH 2. Feedback controller The design of the controller is mainly focused on the robot's performance when implementing following task based on the time-varying relative distance and angle. One of the widely concerned issues is the accuracy of steering control[14]. The controller designed in this paper presets a distance D which will be maintained between the robot and the target person during the following process. The robot's linear velocity v is proportional to the current depth difference ( d - D ) , where d is the current depth of the target person relative to the robot. The angular velocity ω is proportional to the current angle difference ( ω - ω mid ) , where ω mid is the central angle of the robot coordinate system. In this paper, the proportional and integral components of the PID controller are used. The preset D, in our case, can be changed for different applications and the suggested range for D is between 2m and 8m for long-range following tasks. Experiments following up shows that different following distance will affect robot's behavior at a large scale. Controlling parameters should be changed accordingly. The following equation details the discretization method of the controller with velocity as the feedback. e 1 ( k ) = d - D (13) e 2 ( k ) = t - T mid (14) v = K p 1 × e 1 ( k ) + K i 1 ∑ i = 0 k e 1 ( i ) + K d 1 [ e 1 ( k ) - e 1 ( k - 1 ) ] (15) w = K p 2 × e 2 ( k ) + K i 2 ∑ i = 0 k e 2 ( i ) + K d 2 [ e 2 ( k ) - e 2 ( k - 1 ) ] (16) Among them, K p 1 , K i 1 , K d 1 , K p 2 , K i 2 , K d 2 are PID proportional constants, e 1 ( k ) , e 2 ( k ) are the error terms of linear velocity and angular velocity respectively. VI. Experiments 1. Experiments on target detection In a learning problem, in order to avoid overfitting of some specific features, a training set should be constructed properly. From 1360 frames of LiDAR scans taken in unstructured outdoor environment in the human-following scene, over 7 thousand clusters were extracted. The feature described in Section IV is calculated for each cluster. The set of clusters contains targets with different shapes at various following distances and angles, targets when partially occluded, and false targets with similar shapes such as bushes and tree trunks. The ground truth of this dataset is manually labeled. An AdaBoost classifier is trained after dividing the dataset into a training set and a testing set by random. The size of the testing set is 20 % of the total data volume. The results of the binary classification are shown in Table 1. For comparison, a SVM classifier with a Radial basis function (RBF) is learned, which was described in Ref.[13] for human detection. The results are shown in Table 2. In Fig.6, the classification results on the testing set in different feature space show that our feature achieves a better performance than other features based on point-cloud's appearance. Table 1. Confusion matrix of AdaBoost classifier applied Output label True label Total Error rate Target Non-target Target 1882 38 8870 1.97% Non-target 137 6813 Table 2. Confusion matrix of SVM classifier applied Output label True label Total Error rate Target Non-target Target 1715 205 8870 10.71% Non-target 744 6206 Fig. 6Open in figure viewerPowerPoint Performance of different feature descriptions 2. Experiments in field In field test, the implementation of the approach presented above is based on a 16-line LiDAR mounted on a tracked vehicle. An industrial personal computer with 2 gigabytes of memory was used for the real-time clustering, classification and tracking process. The experimental platform is shown in Fig.7. 1) Experiments at different following distance Due to the lag of the robot relative to the target person in the human-following scenario, especially in the direction-oriented following, experiments are conducted on different following distances of 2, 4 and 6 meters. As the result shows, the increase of the following distance will make the robot trajectory more lagging in spatiotemporal position. In addition to engineering methods to adjust the overall speed of the vehicle for better performance, it is also a necessity that the vehicle has the ability to avoid obstacles while approaching target person. As shown in Fig.8, the trajectories 1, 2 and 3 are the following trajectories when the following robot ideally maintains 2, 4, 6 meters away from the target person. The preset distance can be set to a larger number, for the effective detection distance is up to 12 meters in clear vision. 2) Experiments in different scenarios We perform experiments in various outdoor scenarios. The real-time tracking results and the corresponding scene are shown in Fig.9, where scenario (a) is on a bumpy road with trees and poles as false targets, scenario (b) is on a gravel road, scenario (c) and (d) are that the target person is going uphill and downhill respectively, scenario (e) is on the lawn, scenario (f) is at a narrow hallway, scenario (g) and (h) are with multi target and scenario (i) and (j) are obstacle avoidance scenarios where the robot bypasses a corner and a roadblock. Our algorithm is able to conduct a robust following action of mobile robots in such environments. Fig. 7Open in figure viewerPowerPoint Experiment platform illustration 3. Comparison with other methods We compare the method in this paper with those in Refs.[6,7,9], which are respectively based on traditional visual features, deep visual features, and leg features of 2-D LiDAR scan. Due to the limitations of cameras and 2-D LiDARs, these methods are difficult to use outdoors in complex environments. Furthermore, The application scenario of this article has certain requirements for the following distance, so the need for obstacle avoidance caused by the increased following distance should also be taken into consideration. The methods described in Refs.[6,7,9] do not consider obstacle avoidance as an import task, because they follow human over a relatively short distance. Most algorithms such as those described in Ref.[15], do not take long-distance following as one of the goals. Therefore, the robot's autonomous obstacle avoidance is not considered. Table 3 compares the following distance, following speed and system frame rate of the method in this article with those in Refs.[6,7,9]. It can be seen that the method in this paper has better performance in terms of following distance and following speed. Therefore, it is more suitable for outdoor environment. Moreover, the increase in following distance can also improve the security of the system allowing the robot to run at a higher speed. Fig. 8Open in figure viewerPowerPoint Experiments of different following distance. (a) Overall view of the parterre; (b) Trajectory map of robot following target person Table 3. Comparison of system performance Method Maximum following speed(m/s) Average following distance(m) Frame rate(fps) Gupta e t a l . [ 6 ] 0 . 6 0 . 5 6 Chen e t a l . [ 7 ] 1 . 0 1 . 0 20 Cosgun e t a l . [ 9 ] 0 . 5 1 . 2 10 Ours 1 . 5 4 . 0 10 It can be concluded that the following method in this paper is comparable with other methods in terms of functionality. It can run in real-time while performing better in following distance, following speed and autonomous obstacle avoidance. VII. Conclusions In this paper, an integrated system for long-range autonomous human-following tasks is constructed for practical application. The main purpose is to address systematic problems of continuous tracking in complicated situations that may occur in an outdoor human-following scenario, such as terrain bumps, rapid movement of target person, partial occlusion, etc. Fig. 9Open in figure viewerPowerPoint Outdoor experiments in different scenarios Experiments carried out in the field show that this method is practical, real-time and robust for long-range following tasks in complex outdoor environments. Although the effectiveness of the system is proved in most scenarios, due to the limitation of sparse LiDAR point cloud, it may misidentify targets when similar targets are close to each other and heading similar directions. Therefore, in the following work, in order to further enhance the accuracy and uniqueness of target detection, the visual information of the target will be used for target recognition, and a target-level fusion algorithm will be further applied. Biographies GONG Linxi received the B.E. degree in intelligent science and technology from Nanjing University of Science and Technology in 2018. She is currently pursuing the M.S. degree in pattern recognition and intelligent system with Nanjing University of Science and Technology. Her research interests include human-robot interaction and ground intelligent robot. CAI Yunfei (corresponding author) received the Ph.D. degree from Nanjing University of Science and Technology in 2011. He is currently an associate professor at Nanjing University of Science and Technology. His current research interests include intelligent sensing technology, 3-D environment modeling and simultaneous localization and mapping. References 1M.J. Islam, J. Hong and J. 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