Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming

An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points （GCPs） is presented. To decrease time complexity without losing matching precision, using a multilevel search scheme, the coarse matching is processed in typical disparity space image, while the fine matching is processed in disparity-offset space image. In the upper level, GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint. Under the supervision of the highly reliable GCPs, bidirectional dynamic programming framework is employed to solve the inconsistency in the optimization path. In the lower level, to reduce running time, disparity-offset space is proposed to efficiently achieve the dense disparity image. In addition, an adaptive dual support-weight strategy is presented to aggregate matching cost, which considers photometric and geometric information. Further, post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm, where missing stereo information is substituted from surrounding regions. To demonstrate the effectiveness of the algorithm, we present the two groups of experimental results for four widely used standard stereo data sets, including discussion on performance and comparison with other methods, which show that the algorithm has not only a fast speed, but also significantly improves the efficiency of holistic optimization.

Effective approach for outdoor obstacle detection by clustering LIDAR data context

A method of environment mapping using laser-based light detection and ranging （LIDAR） is proposed in this paper. This method not only has a good detection performance in a wide range of detection angles, but also facilitates the detection of dynamic and hollowed-out obstacles. Essentially using this method, an improved clustering algorithm based on fast search and discovery of density peaks （CBFD） is presented to extract various obstacles in the environment map. By comparing with other cluster algorithms, CBFD can obtain a favorable number of clusterings automatically. Furthermore, the experiments show that CBFD is better and more robust in functionality and performance than the K-means and iterative self-organizing data analysis techniques algorithm （ISODATA）.

3D obstacle detection of indoor mobile robots by floor detection and rejection

Obstacle detection is essential for mobile robots to avoid collision with obstacles.Mobile robots usually operate in indoor environments,where they encounter various kinds of obstacles;however,2D range sensor can sense obstacles only in 2D plane.In contrast,by using 3D range sensor,it is possible to detect ground and aerial obstacles that 2D range sensor cannot sense.In this paper,we present a 3D obstacle detection method that will help overcome the limitations of 2D range sensor with regard to obstacle detection.The indoor environment typically consists of a flat floor.The position of the floor can be determined by estimating the plane using the least squares method.Having determined the position of the floor,the points of obstacles can be known by rejecting the points of the floor.In the experimental section,we show the results of this approach using a Kinect sensor.

Research on Obstacle Detection Method of Urban Rail Transit Based on Multisensor Technology

With the rapid development of urban rail transit,passenger traffic is increasing,and obstacle violations are more frequent,and the safety of train operation under high-density traffic conditions is becoming more and more thought provoking.In order to monitor the train operating environment in real time,this paper first adopts multisensing technology based on machine vision and lidar,which is used to collect video images and ranging data of the track area in real time,and then it performs image preprocessing and division of regions of interest on the collected video.Then,the obstacles in the region of interest are detected to obtain the geometric characteristics and position information of the obstacles.Finally,according to the danger degree of obstacles,determine the degree of impact on the train operation,and use the signal system automatic response ormanual response mode to transmit the detection results to the corresponding train,so as to control the train operation.Through simulation analysis and experimental verification,the detection accuracy and control performance of the detection method are confirmed,which provides safety guarantee for the train operation.

Obstacle detection of mobile robot based on data fusion

To study the problem of obstacle detection based on multi-sensors data fusion,the multi-target tracking theory and techniques are introduced into obstacle detection systems,and the exact position of obstacle can be determined.Data fusion problems are discussed directly based on achievable data from some sensors without considering the specific structure of each individual sensor.With respect to normal linear systems and nonlinear systems,the corresponding algorithms are proposed.The validity of the method is confirmed by simulation results.

Obstacle detection using multi-sensor fusion

This paper presents an obstacle detection approach for blind pedestrians by fusing data from camera and laser sensor.For purely vision-based blind guidance system,it is difficult to discriminate low-level obstacles with cluttered road surface,while for purely laser-based system,it usually requires to scan the forward environment,which turns out to be very inconvenient.To overcome these inherent problems when using camera and laser sensor independently,a sensor-fusion model is proposed to associate range data from laser domain with edges from image domain.Based on this fusion model,obstacle's position,size and shape can be estimated.The proposed method is tested in several indoor scenes,and its efficiency is confirmed.

Obstacle detection and autonomous stair climbing of a miniature jumping robot

Miniature jumping robots(MJRs)have difficulty executing autonomous movements in unstructured environments with obstacles because of their limited perception and computing resources.This study investigates the obstacle detection and autonomous stair climbing methods for MJRs.We propose an obstacle detection method based on a combination of attitude and distance detections,as well as MJRs’motion.A MEMS inertial sensor collects the yaw angle of the robot,and a ranging sensor senses the distance between the robot and the obstacle to estimate the size of the obstacle.We also propose an autonomous stair climbing algorithm based on the obstacle detection method.The robot can detect the height and width of stairs and its position relative to the stairs and then repeatedly jump to climb them step by step.Moreover,the height,width,and position are sent to a control terminal through a wireless sensor network to update the information regarding the MJR and stairs in a control interface.Furthermore,we conduct stair detection,modeling,and stair climbing experiments on the MJR and obtain acceptable precisions for autonomous obstacle negotiation.Thus,the proposed obstacle detection and stair climbing methods can enhance the locomotion capability of the MJR in environmental monitoring,search and rescue,etc.

A survey on multi-sensor fusion based obstacle detection for intelligent ground vehicles in off-road environments

With the development of sensor fusion technologies, there has been a lot of research on intelligent ground vehicles, where obstacle detection is one of the key aspects of vehicle driving. Obstacle detection is a complicated task, which involves the diversity of obstacles, sensor characteristics, and environmental conditions. While the on-road driver assistance system or autonomous driving system has been well researched, the methods developed for the structured road of city scenes may fail in an off-road environment because of its uncertainty and diversity.A single type of sensor finds it hard to satisfy the needs of obstacle detection because of the sensing limitations in range, signal features, and working conditions of detection, and this motivates researchers and engineers to develop multi-sensor fusion and system integration methodology. This survey aims at summarizing the main considerations for the onboard multi-sensor configuration of intelligent ground vehicles in the off-road environments and providing users with a guideline for selecting sensors based on their performance requirements and application environments.State-of-the-art multi-sensor fusion methods and system prototypes are reviewed and associated to the corresponding heterogeneous sensor configurations. Finally, emerging technologies and challenges are discussed for future study.

Dynamic obstacles detection of tram based on laser radar

The detection of obstacles in a dynamic environment is a hot and difficult problem.A method of autonomously detecting obstacles based on laser radar is proposed as a safety auxiliary structure of tram.The nearest neighbor method is used for spatial obstacles clustering from laser radar data.By analyzing the characteristics of obstacles,the types of obstacles are determined by time correlation.Experiments were carried out on the developed unmanned aerial vehicle(UAV),and the experimental results verify the effectiveness of the proposed method.

Unmanned vehicle dynamic obstacle detection,tracking and recognition method based on laser sensor

Purpose-In response to these shortcomings,this paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.Design/methodology/approach-The existing dynamic obstacle detection and tracking methods based on geometric features have a high false detection rate.The recognition methods based on the geometric features and motion status of dynamic obstacles are greatly affected by distance and scanning angle,and cannot meet the requirements of real traffic scene applications.Findings-First,based on the geometric features of dynamic obstacles,the obstacles are considered The echo pulse width feature is used to improve the accuracy of obstacle detection and tracking;second,the space-time feature vector is constructed based on the time dimension and space dimension information of the obstacle,and then the support vector machine method is used to realize the recognition of dynamic obstacles to improve the obstacle The accuracy of object recognition.Finally,the accuracy and effectiveness of the proposed method are verified by real vehicle tests.Originality/value-The paper proposes a dynamic obstacle detection and tracking method based on multi-feature fusion and a dynamic obstacle recognition method based on spatio-temporal feature vectors.The accuracy and effectiveness of the proposed method are verified by real vehicle tests.

UAV low-altitude obstacle detection based on the fusion of LiDAR and camera

In this paper,aiming at the flying scene of the small unmanned aerial vehicle(UAV)in the low-altitude suburban environment,we choose the sensor configuration scheme of LiDAR and visible light camera,and design the static and dynamic obstacle detection algorithms based on sensor fusion.For static obstacles such as power lines and buildings in the low-altitude environment,the way that image-assisted verification of point clouds is used to fuse the contour information of the images and the depth information of the point clouds to obtain the location and size of static obstacles.For unknown dynamic obstacles such as rotary-wing UAVs,the IMM-UKF algorithm is designed to fuse the distance measurement information of point clouds and the high precision angle measurement information of image to achieve accurate estimation of the location and velocity of the dynamic obstacles.We build an experimental platform to verify the effectiveness of the obstacle detection algorithm in actual scenes and evaluate the relevant performance indexes.

Binocular Visual Navigation and Obstacle Avoidance of Mobile Robots Based on Speeded-Up Robust Features

This article presents a good robust and real-time system scheme of the mobile robot obstacle detection and navigation, which principle of work is based on the feature descriptor SURF. In this scheme, firstly, the image information of the mobile robot path was captured by the binocular camera; then the feature points were extracted and corresponding matched using SURF to the binocular images as the undetected obstacles; finally fixed the position of the objective by the parallax between the matching points combining with the binocular vision calibration model. Theoretical derivation and experimental results show that this scheme is more accurate for the detection and navigation of the interest points. It has fast matching speed and high accuracy and low error. So, it has certain practical effect and popularizing value for the mobile robot real-time obstacle avoidance and navigation.

A System of Image Recognition-Based Railway Foreign Object Intrusion Monitoring Design

The monitoring system designed in this paper is on account of YOLOv5(You Only Look Once)to monitor foreign objects on railway tracks and can broadcast the monitoring information to the locomotive in real time.First,the general structure of the system is determined through demand analysis and feasibility analysis,the foreign object intrusion recognition algorithm is designed,and the data set required for foreign object intrusion recognition is made.Secondly,according to the functional demands,the system selects a suitable neural web,and the programming is reasonable.At last,the system is simulated to validate its functionality(identification and classification of track intrusion and determination of a safe operating zone).

A Lightweight UAV Visual Obstacle Avoidance Algorithm Based on Improved YOLOv8

The importance of unmanned aerial vehicle(UAV)obstacle avoidance algorithms lies in their ability to ensure flight safety and collision avoidance,thereby protecting people and property.We propose UAD-YOLOv8,a lightweight YOLOv8-based obstacle detection algorithm optimized for UAV obstacle avoidance.The algorithm enhances the detection capability for small and irregular obstacles by removing the P5 feature layer and introducing deformable convolution v2(DCNv2)to optimize the cross stage partial bottleneck with 2 convolutions and fusion(C2f)module.Additionally,it reduces the model’s parameter count and computational load by constructing the unite ghost and depth-wise separable convolution(UGDConv)series of lightweight convolutions and a lightweight detection head.Based on this,we designed a visual obstacle avoidance algorithm that can improve the obstacle avoidance performance of UAVs in different environments.In particular,we propose an adaptive distance detection algorithm based on obstacle attributes to solve the ranging problem for multiple types and irregular obstacles to further enhance the UAV’s obstacle avoidance capability.To verify the effectiveness of the algorithm,the UAV obstacle detection(UAD)dataset was created.The experimental results show that UAD-YOLOv8 improves mAP50 by 3.4%and reduces GFLOPs by 34.5%compared to YOLOv8n while reducing the number of parameters by 77.4%and the model size by 73%.These improvements significantly enhance the UAV’s obstacle avoidance performance in complex environments,demonstrating its wide range of applications.

Deep Reinforcement Learning Based Unmanned Aerial Vehicle (UAV) Control Using 3D Hand Gestures

The evident change in the design of the autopilot system produced massive help for the aviation industry and it required frequent upgrades.Reinforcement learning delivers appropriate outcomes when considering a continuous environment where the controlling Unmanned Aerial Vehicle(UAV)required maximum accuracy.In this paper,we designed a hybrid framework,which is based on Reinforcement Learning and Deep Learning where the traditional electronic flight controller is replaced by using 3D hand gestures.The algorithm is designed to take the input from 3D hand gestures and integrate with the Deep Deterministic Policy Gradient(DDPG)to receive the best reward and take actions according to 3D hand gestures input.The UAV consist of a Jetson Nano embedded testbed,Global Positioning System(GPS)sensor module,and Intel depth camera.The collision avoidance system based on the polar mask segmentation technique detects the obstacles and decides the best path according to the designed reward function.The analysis of the results has been observed providing best accuracy and computational time using novel design framework when compared with traditional Proportional Integral Derivatives(PID)flight controller.There are six reward functions estimated for 2500,5000,7500,and 10000 episodes of training,which have been normalized between 0 to−4000.The best observation has been captured on 2500 episodes where the rewards are calculated for maximum value.The achieved training accuracy of polar mask segmentation for collision avoidance is 86.36%.

Design, manufacture and testing of a prototype obstacle-detection device at blind corners: an automated highway system technology

Traffic accidents on highways are attributed mostly to the”invisibility”of oncoming traffic and road signs.”Speeding”also causes drivers to reduce the effective radius of the vehicle path in the curve,thus trespassing into the lane of the oncoming traffic.The main aim of this paper was to develop a multisensory obstacle-detection device that is affordable,easy to implement and easy to maintain to reduce the risk of road accidents at blind corners.An ultrasonic sensor module with a maximum measuring angle of 15◦was used to ensure that a significant portion of the lane was detected at the blind corner.The sensor covered a minimum effective area of 0.5 m2 of the road for obstacle detection.Yellow light was employed to signify caution while negotiating the blind corner.Two photoresistors(PRs)were used as sensors because of the limited number of pins on the microcontroller(Arduino Uno).However,the device developed for this project achieved obstacle detection at blind corners at relatively low cost and can be accessed by all road users.In real-world applications,the use of piezoelectric accelerometers(vibration sensors)instead of PR sensors would be more desirable in order to detect not only cars but also two-wheelers.

Mobile Based IoT Solution for Helping Visual Impairment Users

Blind people have many tasks to do in their lives. However, blindness generates challenges for them to perform their tasks. Many blind persons use a traditional stick to move around and to perform their tasks. But the obstacles are not detected in traditional stick, it is ineffective for visually impaired people. The blind person has no idea what kind of objects or obstacles are in front of him/her. The blind person has no idea what size the object is or how far away he or she is from it. It is difficult for a blind person to get around. To assist people with vision impairment by making many of their daily tasks simple, comfortable, and organized, they will be able to recognize anything (an obstacle for blind people). A smart stick with mobile application can be used. One of the solutions is a mobile-based Internet of Things solution, which is a stick intended to assist visually impaired people to navigate more easily. It enables blindness and low vision people to navigate and carry out their daily tasks with ease and comfort. A technologically advanced blind stick that enables visually impaired people to move with ease. This paper proposes a system of software and hardware that helps visually impairment people to find their ways in an easy and comfortable way. The proposed system uses smart stick and mobile application to help blind and visually impaired people to identify objects (such as walls, tables, vehicles, people, etc.) in their ways, this can enable them to avoid these objects. In addition, as a result, the system will notify the user through sound from the smartphone. Finally, if he/she gets lost, he will be able to send an SMS with his/her GPS location.