A scan restoration method for robust polar scan matching in dynamic environments

This paper addresses the problem of scan matching which is highly indispensable for mobile robot systems based on range sensors. Recently, polar scan matching (PSM) has been used in solving the problem because it is accurate and fast enough to be performed in real time. However, the performance of PSM degenerates when the portion of scan data from dynamic objects is excessively large. This paper proposes a scan restoration method to overcome this problem and improve the performance of PSM in dynamic environments. The proposed method restores the scan data from dynamic objects to appropriate scan data from static objects. First, whole scan data is segmented and classified as static and dynamic objects. Next, curvature functions are extracted from the classified segments and smoothed by interpolating the segments indicating dynamic obstacles. PSM with the proposed method was tested and evaluated in various real dynamic environments, which reveals that the proposed method can improve the performance of PSM in dynamic environments.     

Intelligent vision system for dynamic environments

This article describes an intelligent vision system that absorbs useful information from its environment and draws useful conclusions. This system can give instructions to locate vacant seats that are currently occupied in a theater. The extraction of useful information without viewing or exposing the inside details of an environment through an active vision system is proposed. Reasoning-based conclusions are drawn for optimum searching. The effectiveness of the proposed method is demonstrated using an experiment. This work was presented in part at the 10th International Symposium on Artificial Life and Robotics, Oita, Japan, February 4–6, 2005     

Global localization for mobile robots using reference scan matching

This paper presents a new approach based on scan matching for global localization with a metric-topological hybrid world model. The proposed method aims to estimate relative pose to the most likely reference site by matching an input scan with reference scans, in which topological nodes are used as reference sites for pose hypotheses. In order to perform scan matching we apply the spectral scan matching (SSM) method that utilizes pairwise geometric relationships (PGR) formed by fully interconnected scan points. The SSM method allows the robot to achieve scan matching without using an initial alignment between two scans and geometric features such as corners, curves, or lines. The localization process is composed of two stages: coarse localization and fine localization. Coarse localization with 2D geometric histogram constructed from the PGR is fast, but not precise sufficiently. On the other hand, fine localization using the SSM method is comparatively slow, but more accurate. This coarse-to-fine framework reduces the computational cost, and makes the localization process reliable. The feasibility of the proposed methods is demonstrated by results of simulations and experiments.     

Robust mobile robot localization in highly non-static environments

In this paper, we propose a robust pose tracking method for mobile robot localization with an incomplete map in a highly non-static environment. This algorithm will work with a simple map that does not include complete information about the non-static environment. With only an initial incomplete map, a mobile robot cannot estimate its pose because of the inconsistency between the real observations from the environment and the predicted observations on the incomplete map. The proposed localization algorithm uses the approach of sampling from a non-corrupted window, which allows the mobile robot to estimate its pose more robustly in a non-static environment even when subjected to severe corruption of observations. The algorithm sequence involves identifying the corruption by comparing the real observations with the corresponding predicted observations of all particles, sampling particles from a non-corrupted window that consists of multiple non-corrupted sets, and filtering sensor measurements to provide weights to particles in the corrupted sets. After localization, the estimated path may still contain some errors due to long-term corruption. These errors can be corrected using nonlinear constrained least-squares optimization. The incomplete map is then updated using both the corrected path and the stored sensor information. The performance of the proposed algorithm was verified via simulations and experiments in various highly non-static environments. Our localization algorithm can increase the success rate of tracking its pose to more than 95% compared to estimates made without its use. After that, the initial incomplete map is updated based on the localization result.     

Two-wheeled mobile robot motion control in dynamic environments

In this paper, a feedback control scheme of a two-wheeled mobile robot is explored in dynamic environments. In the existence of local minima, the design of controller is based on Lyapunov function candidate and considers virtual forces information including detouring force. Simulation results are presented to show the effectiveness of the proposed control scheme.     

动态未知环境下一种新的机器人路径规划方法

针对机器人动态路径规划问题,提出了一种机器人在复杂动态环境中实时路径规划方法.该方法基于滚动窗口的路径规划和避障策略,通过设定可视点子目标、绕行障碍物和对动态障碍物的分析预测,实现机器人在复杂动态环境下的路径规划.针对障碍物分布情况,合理设计可视点法和绕行算法之间转换,有效地解决了局部路径规划的死循环与极小值问题.该方...     

An active vision system for multitarget surveillance in dynamic environments.

This paper presents a novel agent-based method for the dynamic coordinated selection and positioning of active-vision cameras for the simultaneous surveillance of multiple objects-of-interest as they travel through a cluttered environment with a-priori unknown trajectories. The proposed system dynamically adjusts not only the orientation but also the position of the cameras in order to maximize the system's performance by avoiding occlusions and acquiring images with preferred viewing angles. Sensor selection and positioning are accomplished through an agent-based approach. The proposed sensing-system reconfiguration strategy has been verified via simulations and implemented on an experimental prototype setup for automated facial recognition. Both simulations and experimental analyses have shown that the use of dynamic sensors along with an effective online dispatching strategy may tangibly improve the surveillance performance of a sensing system.     

Linear-time robot localization and pose tracking using matching signatures

A feature-based method for global localization of mobile robot using a concept of matching signatures is presented. A group of geometric features, their geometric constraints invariant to frame transform, and location dependent constraints, together are utilized in defining signature of a feature. Plausible global poses are found out by matching signatures of observed features with signatures of global map features. The concept of matching signatures is so developed that the proposed method provides a very efficient solution for global localization. Worst-case complexity of the method for estimating and verifying global poses is linear with the size of global reference map. It will also be shown that with the approach of random sampling the proposed algorithm becomes linear with both the size of global map and number of observed features. In order to avoid pose ambiguity, simultaneous tracking of multiple pose hypotheses staying within the same framework of the proposed method is also addressed. Results obtained from simulation as well as from real world experiment demonstrate the performance and effectiveness of the method.     

Safety assessment of robot trajectories for navigation in uncertain and dynamic environments

This paper presents a probabilistic framework for reasoning about the safety of robot trajectories in dynamic and uncertain environments with imperfect information about the future motion of surrounding objects. For safety assessment, the overall collision probability is used to rank candidate trajectories by considering the probability of colliding with known objects as well as the estimated collision probability beyond the planning horizon. In addition, we introduce a safety assessment cost metric, the probabilistic collision cost, which considers the relative speeds and masses of multiple moving objects in which the robot may possibly collide with. The collision probabilities with other objects are estimated by probabilistic reasoning about their future motion trajectories as well as the ability of the robot to avoid them. The results are integrated into a navigation framework that generates and selects trajectories that strive to maximize safety while minimizing the time to reach a goal location. An example implementation of the proposed framework is applied to simulation scenarios, that explores some of the inherent computational trade-offs.     

Cooperative localization for disconnected sensor networks and a mobile robot in friendly environments

Localization for a disconnected sensor network is highly unlikely to be achieved by its own sensor nodes, since accessibility of the information between any pair of sensor nodes cannot be guaranteed. In this paper, a mobile robot (or a mobile sensor node) is introduced to establish correlations among sparsely distributed sensor nodes which are disconnected, even isolated. The robot and the sensor network operate in a friendly manner, in which they can cooperate to perceive each other for achieving more accurate localization, rather than trying to avoid being detected by each other. The mobility of the robot allows for the stationary and internally disconnected sensor nodes to be dynamically connected and correlated. On one hand, the robot performs simultaneous localization and mapping (SLAM) based on the constrained local submap filter (CLSF). The robot creates a local submap composed of the sensor nodes present in its immediate vicinity. The locations of these nodes and the pose (position and orientation angle) of the robot are estimated within the local submap. On the other hand, the sensor nodes in the submap estimate the pose of the robot. A parallax-based robot pose estimation and tracking (PROPET) algorithm, which uses the relationship between two successive measurements of the robot's range and bearing, is proposed to continuously track the robot's pose with each sensor node. Then, tracking results of the robot's pose from different sensor nodes are fused by the Kalman filter (KF). The multi-node fusion result are further integrated with the robot's SLAM result within the local submap to achieve more accurate localization for the robot and the sensor nodes. Finally, the submap is projected and fused into the global map by the CLSF to generate localization results represented in the global frame of reference. Simulation and experimental results are presented to show the performances of the proposed method for robot-sensor network cooperative localization. Especially, if the robot (or the mobile sensor node) has the same sensing ability as the stationary sensor nodes, the localization accuracy can be significantly enhanced using the proposed method.     

Omnidirectional vision-based ego-pose estimation for an autonomous in-pipe mobile robot

This paper describes the omnidirectional vision-based ego-pose estimation method of an in-pipe mobile robot. An in-pipe mobile robot has been developed for inspecting the inner surface of various pipeline configurations, such as the straight pipeline, the elbow and the multiple-branch. Because the proposed in-pipe mobile robot has four individual drive wheels, it has the ability of flexible motions in various pipelines. The ego-pose estimation is indispensable for the autonomous navigation of the proposed in-pipe robot. An omnidirectional camera and four laser modules mounted on the mobile robot are used for ego-pose estimation. An omnidirectional camera is also used for investigating the inner surface of the pipeline. The pose of the in-pipe mobile robot is estimated from the relationship equation between the pose of a robot and the pixel coordinates of four intersection points where light rays that emerge from four laser modules intersect the inside of the pipeline. This relationship equation is derived from the geometry analysis of an omnidirectional camera and four laser modules. In experiments, the performance of the proposed method is evaluated by comparing the result of our algorithm with the measurement value of a specifically designed sensor, which is a kind of a gyroscope.     

On the use of likelihood fields to perform sonar scan matching localization

Scan matching algorithms have been extensively used in the last years to perform mobile robot localization. Although these algorithms require dense and accurate sets of readings with which to work, such as the ones provided by laser range finders, different studies have shown that scan matching localization is also possible with sonar sensors. Both sonar and laser scan matching algorithms are usually based on the ideas introduced in the ICP (Iterative Closest Point) approach. In this paper a different approach to scan matching, the Likelihood Field based approach, is presented. Three scan matching algorithms based on this concept, the non filtered sNDT (sonar Normal Distributions Transform), the filtered sNDT and the LF/SoG (Likelihood Field/Sum of Gaussians), are introduced and analyzed. These algorithms are experimentally evaluated and compared to previously existing ICP-based algorithms. The obtained results suggest that the Likelihood Field based approach compares favorably with algorithms from the ICP family in terms of robustness and accuracy. The convergence speed, as well as the time requirements, are also experimentally evaluated and discussed.

Efficient model-based tracking for robot vision

This paper proposes a real-time, robust and efficient three-dimensional (3D) model-based tracking algorithm. A virtual visual servoing approach is used for monocular 3D tracking. This method is similar to more classical non-linear pose computation techniques. A concise method for derivation of efficient distance-to-contour interaction matrices is described. An oriented edge detector is used in order to provide real-time tracking of points normal to the object contours. Robustness is obtained by integrating a M-estimator into the virtual visual control law via an iteratively re-weighted least-squares implementation. The method presented in this paper has been validated on several visual servoing experiments considering various objects. Results show the method to be robust to occlusion, changes in illumination and mis-tracking.     

Three-dimensional vision structure for robot applications

Focuses on the structure of robot sensing systems and the techniques for measuring and preprocessing 3-D data. To get the information required for controlling a given robot function, the sensing of 3-D objects is divided into four basic steps: transduction of relevant object properties (primarily geometric and photometric) into a signal; preprocessing the signal to improve it; extracting 3-D object features; and interpreting them. Each of these steps usually may be executed by several alternative techniques (tools). Tools for the transduction of 3-D data and data preprocessing are surveyed. The performance of each tool depends on the specific vision task and its environmental conditions, both of which are variable. Such a system includes so-called tool-boxes, one box for each sensing step, and a supervisor, which controls iterative sensing feedback loops and consists of a rule-based program generator and a program execution controller. Sensing step sequences and tools are illustrated for two 3-D vision applications at SRI International Company: visually guided robot arc welding and locating identical parts in a bin     

Salient human detection for robot vision

In this paper, we propose a salient human detection method that uses pre-attentive features and a support vector machine (SVM) for robot vision. From three pre-attentive features (color, luminance and motion), we extracted three feature maps and combined them as a salience map. By using these features, we estimated a given object’s location without pre-assumptions or semi-automatic interaction. We were able to choose the most salient object even if multiple objects existed. We also used the SVM to decide whether a given object was human (among the candidate object regions). For the SVM, we used a new feature extraction method to reduce the feature dimensions and reflect the variations of local features to classifiers by using an edged-mosaic image. The main advantage of the proposed method is that our algorithm was able to detect salient humans regardless of the amount of movement, and also distinguish salient humans from non-salient humans. The proposed algorithm can be easily applied to human robot interfaces for human-like vision systems.

Mobile robot localization based on effective combination of vision and range sensors

Most localization algorithms are either range-based or vision-based, but the use of only one type of sensor cannot often ensure successful localization. This paper proposes a particle filter-based localization method that combines the range information obtained from a low-cost IR scanner with the SIFT-based visual information obtained from a monocular camera to robustly estimate the robot pose. The rough estimation of the robot pose by the range sensor can be compensated by the visual information given by the camera and the slow visual object recognition can be overcome by the frequent updates of the range information. Although the bandwidths of the two sensors are different, they can be synchronized by using the encoder information of the mobile robot. Therefore, all data from both sensors are used to estimate the robot pose without time delay and the samples used for estimating the robot pose converge faster than those from either range-based or vision-based localization. This paper also suggests a method for evaluating the state of localization based on the normalized probability of a vision sensor model. Various experiments show that the proposed algorithm can reliably estimate the robot pose in various indoor environments and can recover the robot pose upon incorrect localization. Recommended by Editorial Board member Sooyong Lee under the direction of Editor Hyun Seok Yang. This research was conducted by the Intelligent Robotics Development Program, one of the 21st Century Frontier R&D Programs funded by the Ministry of Knowledge Economy of Korea. Yong-Ju Lee received the B.S. degree in Mechanical Engineering from Korea University in 2004. He is now a Student for Ph.D. of Mechanical Engineering from Korea University. His research interests include mobile robotics. Byung-Doo Yim received the B.S. degree in Control and Instrumentation Engineering from Seoul National University of Technology in 2005. Also, he received the M.S. degree in Mechatroncis Engineering from Korea University in 2007. His research interests include mobile robotics. Jae-Bok Song received the B.S. and M.S. degrees in Mechanical Engineering from Seoul National University in 1983 and 1985, respectively. Also, he received the Ph.D. degree in Mechanical Engineering from MIT in 1992. He is currently a Professor of Mechanical Engineering, Korea University, where he is also the Director of the Intelligent Robotics Laboratory from 1993. His current research interests lie mainly in mobile robotics, safe robot arms, and design/control of intelligent robotic systems.     

Convergent matching for model-based computer vision

We consider matching in model-based computer vision as a converging discrete iteration and give a basis for examining the convergence as the movement of the working point in a lattice. Because the matching is non-deterministic we discuss convergence in terms of completing sub-problems within a time slot. This form of low-level scheduling avoids effectively unlimited trials of sub-graphs, a phenomenon that we call the NP-trap. We define high-level scheduling as the need to test each reference class at least once and thereafter focus attention on the most promising candidates. Examples show the bounding of matching time with a time slot and focusing of attention guided by a figure of merit.