An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

Goodin, C.; Carrillo, Justin; Monroe, J. Gabriel; Carruth, Daniel W.; Hudson, Christopher R.

doi:10.3390/s21093211

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…The slope-based obstacle detector follows the method outlined in 19 to measure the local terrain slope directly from a registered 3D point cloud. Each time a new point cloud message is received, the points are checked one by one to see which cell it occupies in the existing occupancy grid.…”

Section: Slope-based Segmentationmentioning

confidence: 99%

The NATURE autonomy stack: an open-source stack for off-road navigation

Goodin,

Moore,

Carruth

et al. 2024

Unmanned Systems Technology XXVI

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Off-road autonomous navigation remains an ongoing challenge for autonomous ground vehicles (AGV). The challenges of navigating in an unstructured environment include identifying and detecting both positive and negative obstacles, distinguishing navigable from non-navigable vegetation, identifying soft soil, and negotiating rough or sloping terrain. While many recent works have dealt with various aspects of the off-road navigation problem, up to now there has not been a free and open-source autonomy stack for off-road that included integrated modules for perception, planning, and control. Therefore, we have recently developed the NATURE (Navigating All Terrains Using Robotic Exploration) autonomy stack as a publicly available resource to facilitate the advancement of off-road navigation research. The NATURE stack is implemented using the Robotic Operating System (ROS) and can be built to work with both ROS-1 and ROS-2. The modular nature of the NATURE stack makes it an ideal resource for researchers who want to evaluate a particular algorithm for perception, planning, or control without developing an entire navigation stack from scratch. NATURE features several options for both global and local path planning including A*, artificial potential field, and spline-based planning, as well as multiple options for perception including a simple geometrically based obstacle finder and more advanced custom traversability algorithm derived from 3D lidar. In this presentation we give an overview of the NATURE stack and show some past uses of the stack in both simulated and field experiments.

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Section: Slope-based Segmentationmentioning

confidence: 99%

The NATURE autonomy stack: an open-source stack for off-road navigation

Goodin,

Moore,

Carruth

et al. 2024

Unmanned Systems Technology XXVI

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“…Goodin et al [ 61 ] presented a Lidar-based model for analyzing the performance of negative obstacle detection. In this model, the sensor has to be installed on AGV to consider vehicle movement and speed, as the detection algorithm is based on curvature.…”

Section: Negative Obstacles Detection and Analysismentioning

confidence: 99%

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Islam¹,

Nabi²,

Ball³

2022

Sensors

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When it comes to some essential abilities of autonomous ground vehicles (AGV), detection is one of them. In order to safely navigate through any known or unknown environment, AGV must be able to detect important elements on the path. Detection is applicable both on-road and off-road, but they are much different in each environment. The key elements of any environment that AGV must identify are the drivable pathway and whether there are any obstacles around it. Many works have been published focusing on different detection components in various ways. In this paper, a survey of the most recent advancements in AGV detection methods that are intended specifically for the off-road environment has been presented. For this, we divided the literature into three major groups: drivable ground and positive and negative obstacles. Each detection portion has been further divided into multiple categories based on the technology used, for example, single sensor-based, multiple sensor-based, and how the data has been analyzed. Furthermore, it has added critical findings in detection technology, challenges associated with detection and off-road environment, and possible future directions. Authors believe this work will help the reader in finding literature who are doing similar works.

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“…| INTRODUCTIONObstacle detection is essential for self-driving Micro Aerial Vehicles (MAVs). Detection of obstacles can be categorized into image-based(Aharchi & Kbir, 2022;Shi et al, 2023), sensor-based(Goodin et al, 2021;Wilshin et al, 2023), and hybrid(Beul et al, 2017;Hu et al, 2020). Image-based approaches use image information, such as gray levels(Mashaly et al, 2016), points(Aguilar et al, 2017;Al-Kaff et al, 2017), edges(Mashaly et al, 2016), and regions(Badrloo et al, 2022b).…”

mentioning

confidence: 99%

Developing an expansion‐based obstacle detection using panoptic segmentation

Pirasteh,

Varshosaz,

Badrloo

et al. 2024

Journal of Field Robotics

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Safe Micro Aerial Vehicle (MAV) navigation requires detecting and avoiding obstacles. For safe MAV navigation, expansion‐based algorithms are effective for detecting obstacles. However, accurate and real‐time obstacle detection is a fundamental challenge. Some traditional methods focus on extracting geometric features from images and applying geometric constraints to identify potential obstacles. Others may leverage machine learning algorithms for object detection and classification, using features such as texture, shape, and context to distinguish obstacles from background clutter. The choice of approach depends on factors such as the specific requirements of the application, the complexity of the scene, and the available computational resources. Since obstacles, in reality, take the form of objects (e.g., persons, walls, pillars, trees, automobiles, and other structures), it is preferable to represent them according to human comprehension and as objects. Therefore, the objective of this study is to reflect on the previous research and address the issues mentioned above by extracting objects from the fisheye image using a panoptic deep‐learning network. The extracted object regions are, then, used to identify obstacles with a novel area‐based expansion rate we developed in a previous study. We compared the accuracy of obstacle detection in our proposed method to the existing method when moving forward and to the right; thus, we improved it between 10% and 18%, respectively. In addition, compared with the existing method, and due to replacing a single object with multiple regions, obstacle‐detection runtime for forward and right direction is 15.71 and 25.5 times faster, respectively, and the required match points have decreased by 49% and 55%.

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An Analytic Model for Negative Obstacle Detection with Lidar and Numerical Validation Using Physics-Based Simulation

Cited by 9 publications

References 25 publications

The NATURE autonomy stack: an open-source stack for off-road navigation

The NATURE autonomy stack: an open-source stack for off-road navigation

Off-Road Detection Analysis for Autonomous Ground Vehicles: A Review

Developing an expansion‐based obstacle detection using panoptic segmentation

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