Dynamic Obstacle Detection Research Articles

Multi-vehicle formation control and obstacle avoidance using negative-imaginary systems theory

This paper presents a compelling combination of the two negative-imaginary (NI) control systems: a consensus-based formation control framework for a hybrid multi-vehicle system and a new dynamic obstacle detection and avoidance algorithm. The incorporation of the two techniques permits robots to self-detect collisions and then self-create a safe path to overcome any unexpected obstacles while traveling to the destination in a given formation. Moreover, owing to the distributed and dynamic architecture, our NI obstacle avoidance method has better adaptability to the change of environment conditions or obstacle/robot dynamics. Also, a rigorous comparative study on the performance of our obstacle avoidance method with respect to that of the artificial potential field function and switching formation-based NI obstacle avoidance algorithms is performed as a benchmark. Finally, simulation and experimental results are then implemented to demonstrate the effectiveness of the proposed theories.

Distributed Camera Architecture for Seamless Detection and Tracking of Dynamic Obstacles

Abstract Tracking of dynamic objects is a key element especially for in-door logistic solutions but also for e.g. manufacturing plants and warehouses. This paper is dealing with a tracking approach which is based on multiple cameras that are permanently mounted on the ceiling. In comparison to currently applied methods of detecting and tracking dynamic objects in the field of AGVs this approach is innovative. This is due to the shift in the capture of tracking data to ceiling cameras exclusively, for the reason of gaining a holistic overview of the complete tracking area. The application case discussed in this paper is for AGVs in the frame of a research project. However, this approach can be applied for different use cases which require a pursuit and tracking of dynamic objects that is based on multiple cameras.

Comprehensive evaluation of static and dynamic obstacle detection method for safe navigation of the visually impaired people

Among the most challenging difficulties that hinder the visually impaired is a safe navigation from one place to another. We will provide a comprehensive framework to evaluate our smartphone-based obstacle detection method for safe navigation for the visually impaired people. This method aims to provide a safe navigation by detecting static and dynamic obstacles in unknown environments while offering maximum flexibility to the user and using the most cost-effective equipment possible. Our obstacle detection method relies on a multiregion analysis technique to examine the results of analyzing different regions of video frames. The user is notified about the existence of an obstacle by receiving alert messages. We (1) introduce a rich dataset, which is available for research purposes and can be used for the future evaluation and development of computer vision-based applications pertinent to static and dynamic obstacle detection in the unknown environment via smartphone. (The dataset is made available by King Abdulaziz University for research purposes only. Please, contact the authors for further information.) In addition, (2) we demonstrate the efficiency of the proposed method through a comprehensive framework of testing and evaluation in various complex and unknown scenes. The usability of the method has been tested by allowing a set of visually impaired people to use our system in their navigation. The experimental results show that our obstacle detection method is easy to use, operates efficiently, can be deployed on an average range smartphone device, and has a high accuracy rate ranging from 85% to 96%.

Dynamic Obstacle Detection and Tracking Based on 3D Lidar

Detection and tracking of dynamic obstacle is one of the research hotspot in autonomous vehicles. In this paper, a dynamic obstacle detection and tracking method based on 3D lidar is proposed. The nearest neighborhood method is used to cluster the data obtained by the laser lidar. The characteristic parameters of the clustering obstacles are analyzed. Multiple hypothesis tracking model (MHT) algorithm and the nearest neighbor association algorithm are used for data association of two consecutive frames of obstacle information. The dynamic and static state of obstacles are analyzed through the temporal and spatial correlation of the obstacle. Finally, we use linear Kalman filter to predict the movement state of the obstacle. The experimental results on a low-speed driverless vehicle “small whirlwind” which is an autonomous sightseeing vehicle show that the method can accurately detect the dynamic obstacles in unknown environment with effectiveness and real-time performance.

P‐202: Late‐News Poster: Dynamic Obstacle Detection to Improve BVI Pedestrian's Navigation Decision using CNNs

Two CNNs models are proposed to detect dynamic obstacles in urban setting so as to improve BVI pedestrian's navigation decision. First, we re‐implement the inspired model, yolov2, on KITTI datasets. Regarding evaluation, the two models perform better than yolov2 from 6.26% to 10.99% on car detection at different difficulty levels.

Multi-Modal Detection and Mapping of Static and Dynamic Obstacles in Agriculture for Process Evaluation.

Today, agricultural vehicles are available that can automatically perform tasks such as weed detection and spraying, mowing, and sowing while being steered automatically. However, for such systems to be fully autonomous and self-driven, not only their specific agricultural tasks must be automated. An accurate and robust perception system automatically detecting and avoiding all obstacles must also be realized to ensure safety of humans, animals, and other surroundings. In this paper, we present a multi-modal obstacle and environment detection and recognition approach for process evaluation in agricultural fields. The proposed pipeline detects and maps static and dynamic obstacles globally, while providing process-relevant information along the traversed trajectory. Detection algorithms are introduced for a variety of sensor technologies, including range sensors (lidar and radar) and cameras (stereo and thermal). Detection information is mapped globally into semantical occupancy grid maps and fused across all sensors with late fusion, resulting in accurate traversability assessment and semantical mapping of process-relevant categories (e.g., crop, ground, and obstacles). Finally, a decoding step uses a Hidden Markov model to extract relevant process-specific parameters along the trajectory of the vehicle, thus informing a potential control system of unexpected structures in the planned path. The method is evaluated on a public dataset for multi-modal obstacle detection in agricultural fields. Results show that a combination of multiple sensor modalities increases detection performance and that different fusion strategies must be applied between algorithms detecting similar and dissimilar classes.

Dynamic obstacle detection based on multi-sensor information fusion

Dynamic obstacle detection is the key to ensure the agricultural robots could move autonomously in the non-structural environments. In this study, a method of dynamic obstacle detection based on multi-sensor information fusion is presented by selecting a Compass Equipment, an Inertial Measurement Unit and a 2D laser scanner as the system’s external sensors. A method based on Kalman filter to fuse data from a Compass Equipment and an Inertial Measurement is presented to obtain the position of agricultural machinery. 2D laser scanner has the feature of scanning widely and getting the angle and distance of each obstacle directly. On this foundation, the absolute position and motion state of the obstacle is obtained by the transformation of the relative coordinates. After filtering, clustering and segmentation of laser data by using the method of Voxel grid method and Euclidean method, the absolute position of the same obstacle in the adjacent sampling period is analyzed to distinguish the static and dynamic obstacles. The experiment verify the effectiveness of the algorithm and have certain significance for the realization of autonomous mobile robot.

Technology of intelligent driving radar perception based on driving brain

Radar is an important sensor to realise intelligent driving environment perception, enabling the detection of static obstacles and dynamic obstacles, and the tracking of a dynamic obstacle. The models, quantities, and installing location of the platform radar sensors as well as the information processing modules differ from each other on different intelligent driving testing platforms, resulting in different quantities and interfaces on the intelligent driving system. Here, the authors build the software architecture of intelligent driving vehicle based on driving brain which is used to adapt to different types of radar sensors and use the variable granularity road ownership radar for radar information fusion. Under the condition of complete driving information, increasing or reducing the number of radar sensors and changing the radar sensor model or installing location will not affect the intelligent driving decision directly. Therefore, the authors meet the demands of multi-radar sensor adapting to different intelligent driving hardware testing platforms.

Dynamic Obstacle Detection Based on Background Compensation in Robot’s Movement Space

In the safety area of industrial production, robots are required to have the ability of quickly detecting the dynamic obstacles which appear in the working space of robots when robots move along a predefined route. In order to solve the problem of dynamic obstacles detection more effectively, the paper proposes a quick detection method based on background compensation for dynamic obstacles in robot movement space, which makes extraction of feature points on dynamic obstacles in robot movement space, establishes the background model of optical flow velocity field between adjacent frames and makes background compensation for images with the use of block-matching algorithm in order to realize quick and accurate dynamic obstacles detection in dynamic environment. The experiment results show that the obstacles detection method can effectively eliminate the background movement caused by the movement of camera at the end of robot and can extract a more complete target object. Moreover, it also has good robustness.

A dynamic filtering obstacle detection method based on ultrasonic array

In research on the navigation and control of an Autonomous Land Vehicle (ALV), the ultrasonic obstacle detection system plays an important role extending the environment cognition capability of the ALV. With a goal of improving the accuracy of ultrasonic obstacle detection, a dynamic data filtering method based on ultrasonic array is presented. The sonar return data is first processed through static filtering according to the geometric relationship of the ultrasonic array. Then, dynamic filtering is executed using the orientation and trajectory information of the vehicle. The dynamic filtering method is compared to the traditional ultrasonic obstacle detection method, which is the static filtering method in a typical field environment. The experiment result demonstrates the validity of the method for solving the fake data problem, and the accuracy of obstacle detection is improved.

Artificial Neural Fuzzy Logic Algorithm for Robot Path Finding

One of the major challenges of the autonomous navigation for mobile robots is the detection and avoidance of static and dynamic obstacles in the robot's movement. This problem can be solved by applying different methods or algorithms in order to achieve optimal results. Besides optimizing, attention should be paid also to the processing time of the signals from the sensors, as it relates to the time response of the system as a whole in order to detect and avoid obstacles without colliding them. In this paper we mixed, Fuzzy Logic Controller (FLC-Sugeno) and Artificial Neuronal Fuzzy Logic (ANFL). The proposed approaches are tested in simulation environment as well as on the real mobile robot with satisfying results in terms of paths and processing-time.

Robust Robot Navigation using Polar Coordinates in Dynamic Environments

This paper presents robust robot navigation using polar coordinates in dynamic environments based on our previous work which was to move toward a target while avoiding static and dynamic obstacles. It utilized vector polar histogram (VPH) with velocity obstacle (VO) for the navigation and obstacle avoidance but had difficulties in detecting dynamic obstacles and reaching the target accurately. To overcome those problems, polar scan matching (PSM) is combined with our previous work. It allows the robot to localize its pose more correctly, which causes an improvement of dynamic obstacle detection. Simulations are performed to verify that our approach outperforms other conventional navigation methods in terms of obstacle avoidance and reaching the target accurately.

Dynamic Obstacle Detection Using Visually Informed Scan Matching

This paper presents a real-time dynamic obstacle detection algorithm using a scan matching method considering image information from a mobile robot equipped with a camera and a laser scanner. By combining image and laser scan data, we extract a scan segment corresponding to the dynamic obstacle. To complement the performance of scan matching, poor in dynamic environments, the extracted scan segment is temporarily removed. After obtaining a good robot position, the position of the dynamic obstacle is calculated based on the robot’s position. Through two experimental scenarios, the performance of the proposed algorithm is demonstrated.

Control and Navigation of an Autonomous Mobile Robot with Dynamic Obstacle Detection and Adaptive Path Finding Algorithm

In this work, a novel approach for navigation, guidance and control of a duo cycle autonomous robot vehicle with dynamic obstacle avoidance and adaptive path finding algorithm is presented. Real time control and simulation of vehicle movements and orientation is made by a stationary computer. In addition, a self developed algorithm is integrated to the system for dynamic obstacle avoidance and adaptive path finding. Sub-optimal paths to the given target point are calculated simultaneously according to dynamic environment with a written algorithm program. Low level control operations are made by microcontroller to minimize time depended errors. Mobile computer is used to collect laser measurement data and provides the communication between vehicle and control station (stationary pc). Laser measurement sensor is used for simultaneous obstacle detection and environment mapping so that an adaptive like path finding can be realized. The simulations and experimental results showed the effectiveness of the proposed approach for navigation applications with adaptive path finding algorithm.

Particle Grid Tracking System Stereovision Based Obstacle Perception in Driving Environments

This paper presents an occupancy grid tracking system based on particles, and the use of this system for dynamic obstacle detection in driving environments. The particles will have a dual nature they will denote hypotheses, as in the particle filtering algorithms, but they will also be the building blocks of our modeled world. The particles have position and speed, and they can migrate in the grid from cell to cell depending on their motion model and motion parameters, but they will also be created and destroyed using a weighting-resampling mechanism specific to particle filter algorithms. An obstacle grid derived from processing a stereovision-generated elevation map is used as measurement information, and the measurement model takes into account the uncertainties of the stereo reconstruction. The dynamic occupancy grid is used for improving the quality of the stereovision-based reconstruction as oriented cuboids. The resulted system is a flexible, real-time tracking solution for dynamic unstructured driving environments, and a useful tool for extracting intermediate dynamic information that can considerably improve object detection and tracking.

Autonomous navigation system using Event Driven-Fuzzy Cognitive Maps

This study developed an autonomous navigation system using Fuzzy Cognitive Maps (FCM). Fuzzy Cognitive Map is a tool that can model qualitative knowledge in a structured way through concepts and causal relationships. Its mathematical representation is based on graph theory. A new variant of FCM, named Event Driven-Fuzzy Cognitive Maps (ED-FCM), is proposed to model decision tasks and/or make inferences in autonomous navigation. The FCM's arcs are updated from the occurrence of special events as dynamic obstacle detection. As a result, the developed model is able to represent the robot's dynamic behavior in presence of environment changes. This model skill is achieved by adapting the FCM relationships among concepts. A reinforcement learning algorithm is also used to finely adjust the robot behavior. Some simulation results are discussed highlighting the ability of the autonomous robot to navigate among obstacles (navigation at unknown environment). A fuzzy based navigation system is used as a reference to evaluate the proposed autonomous navigation system performance.

Dynamic obstacle identification based on global and local features for a driver assistance system

This paper proposes a novel dynamic obstacle recognition system combining global feature with local feature to identify vehicles, pedestrians and unknown backgrounds for a driver assistance system. The proposed system consists of two main procedures: a dynamic obstacle detection model to localize an area containing a moving obstacle, and an obstacle identification model, which is a hybrid of global and local information, for recognizing an obstacle with and without occlusion. A dynamic saliency map is used for localizing an area containing a moving obstacle. For the global feature analysis, we propose a modified GIST using orientation features with MAX pooling, which is robust to translation and size variations of an object. Although the global features are a compact way to represent an object and provide a good accuracy for non-occluded objects, they are sensitive to image translation and occlusion. Thus, a local feature-based identification model is also proposed and combined with the global feature. As such, for the obstacle identification problem, the proposed system mainly follows the global feature-based object identification. If the global feature-based model identifies a candidate area as background, the system verifies the area again using the local feature-based model. As a result, the proposed system is able to provide information on both the appearance of obstacles and the class of an obstacle. Experimental results show that the proposed model can successfully detect obstacle candidates and robustly identify obstacles with and without occlusion.

Performance characterization of the dynamic programming obstacle detection algorithm

A computer vision-based system using images from an airborne aircraft can increase flight safety by aiding the pilot to detect obstacles in the flight path so as to avoid mid-air collisions. Such a system fits naturally with the development of an external vision system proposed by NASA for use in high-speed civil transport aircraft with limited cockpit visibility. The detection techniques should provide high detection probability for obstacles that can vary from subpixels to a few pixels in size, while maintaining a low false alarm probability in the presence of noise and severe background clutter. Furthermore, the detection algorithms must be able to report such obstacles in a timely fashion, imposing severe constraints on their execution time. For this purpose, we have implemented a number of algorithms to detect airborne obstacles using image sequences obtained from a camera mounted on an aircraft. This paper describes the methodology used for characterizing the performance of the dynamic programming obstacle detection algorithm and its special cases. The experimental results were obtained using several types of image sequences, with simulated and real backgrounds. The approximate performance of the algorithm is also theoretically derived using principles of statistical analysis in terms of the signal-to-noise ration (SNR) required for the probabilities of false alarms and misdetections to be lower than prespecified values. The theoretical and experimental performance are compared in terms of the required SNR.