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

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Zhaowei Ma ◽  
Wenchen Yao ◽  
Yifeng Niu ◽  
Bosen Lin ◽  
Tianqing Liu
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Angle Measurement ◽  
Obstacle Detection ◽  
Accurate Estimation ◽  
Measurement Information ◽  
Depth Information ◽  
Performance Indexes ◽  
Low Altitude ◽  
Sensor Configuration

AbstractIn 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.

scholarly journals A Simple Outdoor Environment Obstacle Detection Method Based on Information Fusion of Depth and Infrared

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Yaguang Zhu ◽  
Baomin Yi ◽  
Tong Guo
Keyword(s):  
Edge Detection ◽  
Information Fusion ◽  
Recognition Rate ◽  
Infrared Image ◽  
Depth Map ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Intelligent Vehicles ◽  
Outdoor Environment ◽  
Depth Information

In allusion to the existing low recognition rate and robustness problem in obstacle detection; a simple but effective obstacle detection algorithm of information fusion in the depth and infrared is put forward. The scenario is segmented by the mean-shift algorithm and the pixel gradient of foreground is calculated. After pretreatment of edge detection and morphological operation, the depth information and infrared information are fused. The characteristics of depth map and infrared image in edge detection are used for the raised method, the false rate of detection is reduced, and detection precision is improved. Since the depth map and infrared image are not affected by natural sunlight, the influence on obstacle recognition due to the factors such as light intensity and shadow is effectively reduced and the robustness of the algorithm is also improved. Experiments indicate that the detection algorithm of information fusion can accurately identify the small obstacle in the view and the accuracy of obstacle recognition will not be affected by light. Hence, this method has great significance for mobile robot or intelligent vehicles on obstacle detection in outdoor environment.

A Fast Obstacle Detection Algorithm for Mobile Robots and Its Application

ROBOT ◽  
2011 ◽  
Vol 33 (2) ◽  
pp. 198-201 ◽  
Author(s):  
Xiaochuan ZHAO ◽  
Peizhi LIU ◽  
Min ZHANG ◽  
Lihui YANG ◽  
Jianchang SHI
Keyword(s):  
Mobile Robots ◽  
Detection Algorithm ◽  
Obstacle Detection

scholarly journals Efficient Reactive Obstacle Avoidance Using Spirals for Escape

Drones ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 51
Author(s):  
Fábio Azevedo ◽  
Jaime S. Cardoso ◽  
André Ferreira ◽  
Tiago Fernandes ◽  
Miguel Moreira ◽  
...  
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Mobile Robotics ◽  
Point Clouds ◽  
Depth Camera ◽  
Main Task ◽  
Robust Method ◽  
Aerial Vehicles ◽  
Correct Execution ◽  
Low Altitude ◽  
High Degree

The usage of unmanned aerial vehicles (UAV) has increased in recent years and new application scenarios have emerged. Some of them involve tasks that require a high degree of autonomy, leading to increasingly complex systems. In order for a robot to be autonomous, it requires appropriate perception sensors that interpret the environment and enable the correct execution of the main task of mobile robotics: navigation. In the case of UAVs, flying at low altitude greatly increases the probability of encountering obstacles, so they need a fast, simple, and robust method of collision avoidance. This work covers the problem of navigation in unknown scenarios by implementing a simple, yet robust, environment-reactive approach. The implementation is done with both CPU and GPU map representations to allow wider coverage of possible applications. This method searches for obstacles that cross a cylindrical safety volume, and selects an escape point from a spiral for avoiding the obstacle. The algorithm is able to successfully navigate in complex scenarios, using both a high and low-power computer, typically found aboard UAVs, relying only on a depth camera with a limited FOV and range. Depending on the configuration, the algorithm can process point clouds at nearly 40 Hz in Jetson Nano, while checking for threats at 10 kHz. Some preliminary tests were conducted with real-world scenarios, showing both the advantages and limitations of CPU and GPU-based methodologies.

A noise-robust obstacle detection algorithm for mobile robots using active 3D sensors

2014 ◽  
Author(s):  
Andrea Claudi ◽  
Daniele Accattoli ◽  
Paolo Sernani ◽  
Paolo Calvaresi ◽  
Aldo Franco Dragoni
Keyword(s):  
Mobile Robots ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Noise Robust

Obstacle detection based on depth fusion of lidar and radar in challenging conditions

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Guotao Xie ◽  
Jing Zhang ◽  
Junfeng Tang ◽  
Hongfei Zhao ◽  
Ning Sun ◽  
...  
Keyword(s):  
False Alarm ◽  
Experimental Tests ◽  
Point Clouds ◽  
Calibration Method ◽  
Obstacle Detection ◽  
Environmental Perception ◽  
Fusion Model ◽  
Content Type ◽  
Self Calibration ◽  
Temporal Relationships

Purpose To the industrial application of intelligent and connected vehicles (ICVs), the robustness and accuracy of environmental perception are critical in challenging conditions. However, the accuracy of perception is closely related to the performance of sensors configured on the vehicle. To enhance sensors’ performance further to improve the accuracy of environmental perception, this paper aims to introduce an obstacle detection method based on the depth fusion of lidar and radar in challenging conditions, which could reduce the false rate resulting from sensors’ misdetection. Design/methodology/approach Firstly, a multi-layer self-calibration method is proposed based on the spatial and temporal relationships. Next, a depth fusion model is proposed to improve the performance of obstacle detection in challenging conditions. Finally, the study tests are carried out in challenging conditions, including straight unstructured road, unstructured road with rough surface and unstructured road with heavy dust or mist. Findings The experimental tests in challenging conditions demonstrate that the depth fusion model, comparing with the use of a single sensor, can filter out the false alarm of radar and point clouds of dust or mist received by lidar. So, the accuracy of objects detection is also improved under challenging conditions. Originality/value A multi-layer self-calibration method is conducive to improve the accuracy of the calibration and reduce the workload of manual calibration. Next, a depth fusion model based on lidar and radar can effectively get high precision by way of filtering out the false alarm of radar and point clouds of dust or mist received by lidar, which could improve ICVs’ performance in challenging conditions.

Extracting Geometric Edges from 3D Point Clouds Based on Normal Vector Change

2014 ◽  
Vol 571-572 ◽  
pp. 729-734
Author(s):  
Jia Li ◽  
Huan Lin ◽  
Duo Qiang Zhang ◽  
Xiao Lu Xue
Keyword(s):  
Point Clouds ◽  
Detection Algorithm ◽  
Growth Strategy ◽  
Normal Vector ◽  
Geometric Morphometric ◽  
3D Point Clouds ◽  
Real Scene ◽  
Edge Points ◽  
Normal Vectors ◽  
Change Intensity

Normal vector of 3D surface is important differential geometric property over localized neighborhood, and its abrupt change along the surface directly reflects the variation of geometric morphometric. Based on this observation, this paper presents a novel edge detection algorithm in 3D point clouds, which utilizes the change intensity and change direction of adjacent normal vectors and is composed of three steps. First, a two-dimensional grid is constructed according to the inherent data acquisition sequence so as to build up the topology of points. Second, by this topological structure preliminary edge points are retrieved, and the potential directions of edges passing through them are estimated according to the change of normal vectors between adjacent points. Finally, an edge growth strategy is designed to regain the missing edge points and connect them into complete edge lines. The results of experiment in a real scene demonstrate that the proposed algorithm can extract geometric edges from 3D point clouds robustly, and is able to reduce edge quality’s dependence on user defined parameters.

Negative Obstacle Detection Using LiDAR Sensors for a Robotic Wheelchair

2018 ◽  
Author(s):  
Taylor E. Baum ◽  
Kelilah L. Wolkowicz ◽  
Joseph P. Chobot ◽  
Sean N. Brennan
Keyword(s):  
Time Algorithm ◽  
Detection Algorithm ◽  
Obstacle Detection ◽  
Detection Accuracy ◽  
Time Varying ◽  
Wheelchair Users ◽  
Development Platform ◽  
Robotic Wheelchairs ◽  
Robotic Wheelchair ◽  
Negative Obstacle

The objective of this work is to develop a negative obstacle detection algorithm for a robotic wheelchair. Negative obstacles — depressions in the surrounding terrain including descending stairwells, and curb drop-offs — present highly dangerous navigation scenarios because they exhibit wide characteristic variability, are perceptible only at close distances, and are difficult to detect at normal operating speeds. Negative obstacle detection on robotic wheelchairs could greatly increase the safety of the devices. The approach presented in this paper uses measurements from a single-scan laser range-finder and a microprocessor to detect negative obstacles. A real-time algorithm was developed that monitors time-varying changes in the measured distances and functions through the assumption that sharp increases in this monitored value represented a detected negative obstacle. It was found that LiDAR sensors with slight beam divergence and significant error produced impressive obstacle detection accuracy, detecting controlled examples of negative obstacles with 88% accuracy for 6 cm obstacles and above on a robotic development platform and 90% accuracy for 7.5 cm obstacles and above on a robotic wheelchair. The implementation of this algorithm could prevent life-changing injuries to robotic wheelchair users caused by negative obstacles.

scholarly journals Use of geotechnologies in mining planning in Quarries carbonated rocks - Contribution to the digital transition (Industry 4.0)

2021 ◽  
Vol 906 (1) ◽  
pp. 012015
Author(s):  
João Duarte ◽  
Francisco Sousa ◽  
Bruno Valente
Keyword(s):  
Industry 4.0 ◽  
Economic Valuation ◽  
Reference Value ◽  
Point Clouds ◽  
Detection Algorithm ◽  
Second Phase ◽  
Ornamental Stone ◽  
Use Of Resources ◽  
Photogrammetric Method

Abstract As part of the strategy for Industry 4.0, this work was developed to outline a methodology that is an important contribution to improve the efficiency and productivity of processes in the ornamental stone extraction industry. Since this sector is important for the Portuguese economy, it is imperative to optimize processes to improve their efficiency in the use of resources, economic valuation, and economic viability. Knowing that one of the main factors to take into account in the feasibility of an exploration of ornamental rocks is the density, persistence and attitude of the discontinuities present in the rock mass, a methodology is proposed that aims to map and characterize the existing discontinuities in the using the latest digital technologies and whenever possible open access (CloudCompare, Stereonet, 3D Block Expert). To this end, work was initially carried out on an active exploration front, identifying and characterizing, through the traditional method (compass and clinometer) and photogrammetry, existing discontinuities and statistically analysing their occurrence. The data analysis shows a variation in the attitude of the discontinuities in a range of -17.72 ° to 14.7 °, this variation corresponding to the strike. As a percentage, there is also a variation in the range of values, from -5.30% to 4.91%, with the reference value being the value obtained by the photogrammetric method. This step was also used to compare the acquired data and verify the variations between them depending on the method used. Photogrammetry was used with another complementary purpose, but very important for the proposed methodology, which is related to the 3D modelling of the fronts and the subsequent projection or extraction of the existing discontinuity plans. The determination of the attitude of the discontinuities was obtained through the manipulation of the point clouds obtained by the photogrammetric modelling, based on the technique of Structure for Motion [SfM] and application of the RANSAC Shape Detection algorithm of the CloudCompare® program, which allows the determination of the attitude of the discontinuities. The characterization of the discontinuities by the photogrammetric method provided the data that was used in the present study to calculate the blocometry in that sector. This was calculated using the 3D BlockExpert software, based on the exploration sequences. The program calculated the predicted volumes in each one, based on a standard dimension for the block of 2.7 × 3.0 × 2.0 meters. As a result, it was possible to compare a number of blocks the value predicted by the 449 modellings and the number of blocks produced 490. This difference of approximately 10% for this order of magnitude is acceptable and confirms the reliability of the proposed methodology. This evaluation using Geotechnologies allows data modelling to be effectively an important process in the planning of the extractive process, and with the development of this approach, it may introduce in a second phase the decision automation of the extractive process, based on economic and commercial criteria and last and third stage, the automation of the extractive process.

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