Robust Obstacle Detection Research Articles

Improved Hybrid Model for Obstacle Detection and Avoidance in Robot Operating System Framework (Rapidly Exploring Random Tree and Dynamic Windows Approach).

The integration of machine learning and robotics brings promising potential to tackle the application challenges of mobile robot navigation in industries. The real-world environment is highly dynamic and unpredictable, with increasing necessities for efficiency and safety. This demands a multi-faceted approach that combines advanced sensing, robust obstacle detection, and avoidance mechanisms for an effective robot navigation experience. While hybrid methods with default robot operating system (ROS) navigation stack have demonstrated significant results, their performance in real time and highly dynamic environments remains a challenge. These environments are characterized by continuously changing conditions, which can impact the precision of obstacle detection systems and efficient avoidance control decision-making processes. In response to these challenges, this paper presents a novel solution that combines a rapidly exploring random tree (RRT)-integrated ROS navigation stack and a pre-trained YOLOv7 object detection model to enhance the capability of the developed work on the NAV-YOLO system. The proposed approach leveraged the high accuracy of YOLOv7 obstacle detection and the efficient path-planning capabilities of RRT and dynamic windows approach (DWA) to improve the navigation performance of mobile robots in real-world complex and dynamically changing settings. Extensive simulation and real-world robot platform experiments were conducted to evaluate the efficiency of the proposed solution. The result demonstrated a high-level obstacle avoidance capability, ensuring the safety and efficiency of mobile robot navigation operations in aviation environments.

A low cost collision avoidance system based on a ToF camera for SLAM approaches

Indoor positioning is a problem that has not yet been solved efficiently and accurately. In outdoors the most effective solution is the Global Position System (GPS), but it cannot be used indoors due to the weakening of the signal, so other solutions have been studied. These approaches could be applied to define a map for the guidance of blind people, tourism or navigation for autonomous robots. In this paper, the study, design, implementation and evaluation of a robust obstacle detection and mapping system is proposed. Thus, it can be used to alert of near objects presence and avoid possible collisions in an indoor navigation. The system is based on a Time-of-Flight (ToF) camera and a Single Board Computer (SBC) like Raspberry PI or NVIDIA Jetson Nano. In order to evaluate the system several real experiments were carried out. This kind of system can be integrated on a wheelchair and help the handicapped person to move indoors or take data from an indoor environment and recreate it in a 2D or 3D images.

Cooperative UAV-UGV Autonomous Power Pylon Inspection: An Investigation of Cooperative Outdoor Vehicle Positioning Architecture.

Realizing autonomous inspection, such as that of power distribution lines, through unmanned aerial vehicle (UAV) systems is a key research domain in robotics. In particular, the use of autonomous and semi-autonomous vehicles to execute the tasks of an inspection process can enhance the efficacy and safety of the operation; however, many technical problems, such as those pertaining to the precise positioning and path following of the vehicles, robust obstacle detection, and intelligent control, must be addressed. In this study, an innovative architecture involving an unmanned aircraft vehicle (UAV) and an unmanned ground vehicle (UGV) was examined for detailed inspections of power lines. In the proposed strategy, each vehicle provides its position information to the other, which ensures a safe inspection process. The results of real-world experiments indicate a satisfactory performance, thereby demonstrating the feasibility of the proposed approach.

Uncertainty-Aware Visual Perception System for Outdoor Navigation of the Visually Challenged.

Every day, visually challenged people (VCP) face mobility restrictions and accessibility limitations. A short walk to a nearby destination, which for other individuals is taken for granted, becomes a challenge. To tackle this problem, we propose a novel visual perception system for outdoor navigation that can be evolved into an everyday visual aid for VCP. The proposed methodology is integrated in a wearable visual perception system (VPS). The proposed approach efficiently incorporates deep learning, object recognition models, along with an obstacle detection methodology based on human eye fixation prediction using Generative Adversarial Networks. An uncertainty-aware modeling of the obstacle risk assessment and spatial localization has been employed, following a fuzzy logic approach, for robust obstacle detection. The above combination can translate the position and the type of detected obstacles into descriptive linguistic expressions, allowing the users to easily understand their location in the environment and avoid them. The performance and capabilities of the proposed method are investigated in the context of safe navigation of VCP in outdoor environments of cultural interest through obstacle recognition and detection. Additionally, a comparison between the proposed system and relevant state-of-the-art systems for the safe navigation of VCP, focused on design and user-requirements satisfaction, is performed.

Robust Obstacle Detection and Recognition for Driver Assistance Systems

This paper proposes a robust obstacle detection and recognition method for driver assistance systems. Unlike existing methods, our method aims to detect and recognize obstacles on the road rather than all the obstacles in the view. The proposed method involves two stages aiming at an increased quality of the results. The first stage is to locate the positions of obstacles on the road. In order to accurately locate the on-road obstacles, we propose an obstacle detection method based on the U-V disparity map generated from a stereo vision system. The proposed U-V disparity algorithm makes use of the V-disparity map that provides a good representation of the geometric content of the road region to extract the road features, and then detects the on-road obstacles using our proposed realistic U-disparity map that eliminates the foreshortening effects caused by the perspective projection of pinhole imaging. The proposed realistic U-disparity map greatly improves the detection accuracy of the distant obstacles compared with the conventional U-disparity map. Second, the detection results of our proposed U-V disparity algorithm are put into a context-aware Faster-RCNN that combines the interior and contextual features to improve the recognition accuracy of small and occluded obstacles. Specifically, we propose a context-aware module and apply it into the architecture of Faster-RCNN. The experimental results on two public datasets show that our proposed method achieves state-of-the-art performance under various driving conditions.

Multimodal Sensor Fusion for Robust Obstacle Detection and Classification in the Maritime RobotX Challenge

This paper describes a novel probabilistic sensor fusion framework aimed at improving obstacle detection accuracy and classification of various targets experienced in the Maritime RobotX Challenge. In both the 2014 and 2016 Maritime RobotX Challenges, it was found that detecting obstacles using LIDAR only and classifying obstacles using vision only can be challenged by environmental conditions, such as glare from the sun or by objects such as the spherical black buoys from the obstacle field that disperses LIDAR rays. In this paper, a new multimodal sensor fusion approach is proposed that combines data streams from perception sensors, such as LIDAR, RADAR, and cameras, to improve the robustness of detection and classification performance over a single sensor method. Using data collected from both the 2014 and 2016 Maritime RobotX Challenges, an evaluation of the perception framework is provided. The proposed detection and classification framework is now being transferred to the queensland university of technology (QUT) autonomous surface vehicle to improve overall mapping accuracy and task execution.

Robust obstacle detection for advanced driver assistance systems using distortions of inverse perspective mapping of a monocular camera

This paper presents a robust method for generic obstacle detection and collision warning in Advanced Driver Assistance Systems (ADAS). The highlight of our method is the ability to detect all obstacles without prior knowledge and detect partially occluded obstacles including the obstacles that have not completely appeared in the frame (truncated obstacles). Our results show an improvement of 90% more true positives per frame compared to one of the state-of-the-art methods. Our proposed method is robust to variations in illumination and to a wide variety of vehicles and obstacles that are encountered while driving. Distortions are introduced when Inverse Perspective Mapping (IPM) projects the camera image onto the road surface plane. In this paper, we first show that the angular distortion in the IPM domain belonging to obstacle edges varies as a function of their corresponding 2D location in the camera plane. We use this information to perform proposal generation. We propose a novel proposal assessment method based on fusing statistical properties from both the IPM image and the camera image to perform robust outlier elimination and false positive reduction. We also present an annotated obstacle detection dataset derived from various source videos that can serve as a benchmark for the evaluation of future obstacle detection algorithms. The source videos containing diverse illumination and traffic conditions are derived from multiple publicly available datasets.

Real-Time Obstacle Detection and Tracking for Sense-and-Avoid Mechanism in UAVs

Obstacle detection and tracking is an important research topic in computer vision with a number of practical applications. Though an ample amount of research has been done in this domain, implementing automatic obstacle detection and tracking in real time is still a big challenge. To address this issue, we propose a fast and robust obstacle detection and tracking approach by integrating an adaptive obstacle detection strategy within a kernelized correlation filter (KCF) framework in this paper. A suitable salient object detection method autoinitializes the KCF tracker for this purpose. Moreover, an adaptive obstacle detection strategy is proposed to refine the location and boundary of the object when the confidence value of the tracker drops below a predefined threshold. In addition, a reliable postprocessing technique is implemented to accurately localize the obstacle from a saliency map recovered from the search region. The proposed approach has been extensively tested through quantitative and qualitative evaluations on a number of challenging datasets. The experiments demonstrate that the proposed approach significantly outperforms the state-of-the-art methods in terms of tracking speed and accuracy.

Fast road obstacle detection method based on maximally stable extremal regions

Road obstacle detection is an important component of the advanced driver assistance system, and to improve the speed and accuracy of road obstacle detection method is a vital task. In this article, fast image region-matching method based on the maximally stable extremal regions method is proposed to improve the speed of image matching. The theoretical feasibility of detection method combining monocular camera with inertial measurement unit (IMU) is clarified. The fast road obstacle detection method based on maximally stable extremal regions combining fast image region-matching method based on maximally stable extremal regions and the vision-IMU-based obstacle detection method is proposed to bypass obstacle classification and to reduce time and space complexity for road environment perception. The AdaBoost cascade detector, the speeded-up robust features-based obstacle detection method, and the proposed method are used to detect obstacles in outdoor contrast tests. Test results show that the proposed method has higher accuracy, and the reason of high accuracy is analyzed. The processing time of AdaBoost cascade detector, speeded-up robust features-based obstacle detection method, and proposed method are compared, and the results show that the proposed method has faster processing speed, and the reason of faster processing speed is analyzed.

Obstacle detection technique using multi sensor integration for small unmanned aerial vehicle

<p class="Abstract">Achieving a robust obstacle detection system for small UAV is very challenging. Due to size and weight constraints, very limited detection sensors can be equipped in the system. Prior works focused on a single sensing device which is either camera or range sensors based. However, these sensors have their own advantages and disadvantages in detecting the appearance of the obstacles. In this paper, combination of both sensors based is proposed for a small UAV obstacle detection system. A small Lidar sensor is used as the initial detector and queue for image capturing by the camera. Next, SURF algorithm is applied to find the obstacle sizes estimation by searching the connecting feature points in the image frame. Finally, safe avoidance path for UAV is determined through the exterior feature points from the estimated width of the obstacle. The proposed method was evaluated by conducting experiments in real time with indoor environment. In the experiment conducted, we successfully detect and determine a safe avoidance path for the UAV on 6 different sizes and textures of the obstacles including textureless obstacles.</p>

Learning Framework for Robust Obstacle Detection, Recognition, and Tracking

This paper introduces a general framework for detection, recognition, and tracking preceding vehicles and pedestrians based on a deep learning approach. The proposed framework combines a novel deep learning approach with the use of multiple sources of local patterns and depth information to yield robust on-road vehicle and pedestrian detection, recognition, and tracking. The proposed system is first based on robust obstacle detection to identify obstacles appearing along the road that are likely to be vehicles and pedestrians, implemented as an efficient adaptive U-V disparity algorithm. Second, the results from the obstacle detection stage are input into a novel vehicle and pedestrian recognition system based on a deep learning model that processes multiple sources of depth information and local patterns. Finally, the results from the recognition stage are used to track detected vehicles or pedestrians in the next frame by means of a proposed tracking and validation model. The proposed framework has been thoroughly evaluated by inputting several vehicle and pedestrian data sets that were collected under various driving conditions. Experimental results show that this framework provides robust vehicle and pedestrian detection, recognition, and tracking with high accuracy, and also satisfies the real-time requirements of driver assistance systems.

Robust underwater obstacle detection and collision avoidance

A robust obstacle detection and avoidance system is essential for long term autonomy of autonomous underwater vehicles (AUVs). Forward looking sonars are usually used to detect and localize obstacles. However, high amounts of background noise and clutter present in underwater environments makes it difficult to detect obstacles reliably. Moreover, lack of GPS signals in underwater environments leads to poor localization of the AUV. This translates to uncertainty in the position of the obstacle relative to a global frame of reference. We propose an obstacle detection and avoidance algorithm for AUVs which differs from existing techniques in two aspects. First, we use a local occupancy grid that is attached to the body frame of the AUV, and not to the global frame in order to localize the obstacle accurately with respect to the AUV alone. Second, our technique adopts a probabilistic framework which makes use of probabilities of detection and false alarm to deal with the high amounts of noise and clutter present in the sonar data. This local probabilistic occupancy grid is used to extract potential obstacles which are then sent to the command and control (C2) system of the AUV. The C2 system checks for possible collision and carries out an evasive maneuver accordingly. Experiments are carried out to show the viability of the proposed algorithm.

A robust obstacle detection method for robotic vacuum cleaners

Conventional robotic vacuum cleaners (RVCs) with ultrasonic or infrared (IR) sensors present problems in detecting obstacles when they clean the floor in complex situations, for example, under tables or chairs with thin legs. This paper presents a robust obstacle detection (OD) method based on the triangulation principle for RVCs operating in various home environments. The proposed method uses the IR emitter of the RVC to project a horizontal IR beam toward the floor, following which the RVC's wide-angle vision camera captures an image that includes the IR line reflected by the floor or an obstacle. Obstacles are detected by using the image coordinates of the pixels that belong to the IR line in the captured image. Accurate separation of the IR line from the image background is accomplished by defining and minimizing an energy function based on the characteristics of the IR line. The proposed method was tested on the embedded RVC system and was shown capable of achieving OD performance compared with existing methods.

Robust obstacle detection based on a novel disparity calculation method and G-disparity

This paper presents a disparity calculation algorithm based on stereo-vision for obstacle detection and free space calculation. This algorithm incorporates line segmentation, multi-pass aggregation and efficient local optimisation in order to produce accurate disparity values. It is specifically designed for traffic scenes where most of the objects can be represented by planes in the disparity domain. The accurate horizontal disparity gradient for the side planes are also extracted during the disparity optimisation stage. Then, an obstacle detection algorithm based on the U–V-disparity is introduced. Instead of using the Hough transform for line detection which is extremely sensitive to the parameter settings, the G-disparity image is proposed for the detection of side planes. Then, the vertical planes are detected separately after removing all the side planes. Faster detection speed, lower parameter sensitivity and improved performance are achieved comparing with the Hough transform based detection. After the obstacles are located and removed from the disparity map, most of the remaining pixels are projections from the road surface. Using a spline as the road model, the vertical profile of the road surface is estimated. Finally, the free-space is calculated based on the vertical road profile which is not restricted by the planar road surface assumption.

Collision Avoidance for Vessels using a Low-Cost Radar Sensor

Collision avoidance for vessels highly depends on a robust obstacle detection. This is commonly achieved by use of high precision radar sensing. For smaller vessels however, the use of low-cost sensors is typical. The idea of this work is to improve the robustness of collision avoidance by integrating a sensor model together with the collision avoidance algorithm in order to consider the accuracy of the measurements. Furthermore, a target tracking algorithm based on an interacting Multi-Model Filter (IMM) is used for robust obstacle detection.

A Novel Color Coherence Vector Based Obstacle Detection Algorithm for Textured Environments

 Abstract—This paper presents a novel obstacle detection algorithm that makes use of color information and color coherence vectors for robust obstacle detection. The algorithm makes use of color cue to classify a pixel in an image as an obstacle or a path. Color is one of the prominent image features. Color information is readily available as input from a color camera. Our algorithm makes use of coherence vectors for representation and matching instead of histograms. A color histogram provides no spatial information. It merely describes the color information present in an image. Color coherence vectors represent pixels as either coherent or incoherent. Color coherence vectors prevent coherent pixels from getting matched with incoherent pixels. The color histogram cannot make such fine distinction. The algorithm is tested with a challenging indoor and outdoor image set. Test results show that our algorithm performs better than available color based obstacle detection approaches.

A robust negative obstacle detection method using seed-growing and dynamic programming for visually-impaired/blind persons

Though a significant amount of work has been done on detecting obstacles, not much attention has been given to the detection of drop offs, e.g., sidewalk curbs, downward stairs, and other hazards. In this paper, we propose algorithms for detecting negative obstacles in an urban setting using stereo vision and two-stage dynamic programming (TSDP) technique. We are developing computer vision algorithms for sensing important terrain features as an aid to blind navigation, which interpret visual information obtained from images collected by cameras mounted on camera legs nearly as high as young person. This paper focuses specifically on a novel computer vision algorithm for detecting negative obstacles (i.e. anything below the level of the ground, such as holes and drop-offs), which are important and ubiquitous features on and near sidewalks and other walkways. The proposed algorithm is compared to other algorithms such as belief propagation and random growing correspondence seeds (GCS). According to the results, the proposed method achieves higher speed, more accurate disparity map and lower RMS errors. The speed of the proposed algorithm is about 28% higher than the random GCS algorithm. We demonstrate experimental results on typical sidewalk scenes to show the effectiveness of the proposed method.

A Fast and Robust Obstacle Detection Algorithm for Off-road Autonomous Mobile Robots

In order to detect the obstacles from the disparity image correctly and resist the interference of the noise robustly,a fast and robust obstacle detection algorithm for off-road autonomous mobile robots is proposed.Firstly,a complete definition of obstacle point integrating the restrictions of height,continuity and slope is given,and then the idea of region growing is used to detect the obstacles with these restrictions.Finally,the algorithm is optimized by using the redundancy detection method and introducing the multi-dimensional strategy.In addition,the principle of the redundancy detection method is proved.Experiment results show that the algorithm is effective and robust in different environments.The average runtime of the algorithm is 25ms,which is fast enough for real time application.

Obstacle Detection Based on Occupancy Grid Maps Using Stereovision System

We previously reported on an obstacle detection method using a stereovision system. The system generated disparity images that include three-dimensional spatial information. Using these images, obstacles could be detected, but some false positives were generated. In this paper, we attempt to eliminate this problem and propose a method that generates Occupancy Grid Maps based on measurements from a stereovision system which leads to robust obstacle detection. Furthermore, it is confirmed that high distance accuracy can be achieved by using our method.

Robust obstacle detection in general road environment based on road extraction and pose estimation

AbstractThis paper proposes a method of obstacle detection using vehicle‐mounted stereo cameras. There are various objects in the road environment; these can be divided into objects, such as pedestrians, which have the possibility of colliding with the vehicle, and objects, such as road patterns, which do not have such a possibility. In this paper, an obstacle is defined as an object which is located in the space through which the vehicle will travel. In order to decide whether an object is an obstacle from the pattern in the image, it is necessary to estimate the position of the pattern in relation to the road surface. For this purpose, the planar region is determined by dynamically estimating the homography (projection transformation) matrix between the images corresponding to the plane. The pose of the camera with respect to the road is determined. Then the spatial position obtained by stereo measurement is converted to road coordinates, based on the orientation of the road surface. By applying segmentation based on the converted result of stereo measurement, obstacles are detected. On ordinary roads it is required to detect various obstacles, ranging from relatively small obstacles such as pedestrians and bicycles to large obstacles such as cars. The proposed method can detect obstacles without explicit modeling. The effectiveness of the proposed method is demonstrated by experiments using various actual images. © 2007 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 90(12): 12–22, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.20413