Polylidar3D-Fast Polygon Extraction from 3D Data

Castagno, Jeremy D.; Atkins, Ella M.

doi:10.3390/s20174819

Cited by 8 publications

(7 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…First, the Intel RealSense D455 camera provides an RGBD image of the curb. This image is then processed by Polylidar3D [ 33 ] to extract all flat surfaces as polygons, which are shown as the green lines in Step 1. In this example, two polygons were returned, the ground surface and sidewalk surface.…”

Section: Methodsmentioning

confidence: 99%

“…In this example, two polygons were returned, the ground surface and sidewalk surface. Each polygon is represented as an ordered list of 3D points that are guaranteed coplanar to a configurable error threshold using Polylidar3D software [ 33 ]. All polygons extracted are in the camera reference frame {C} but must be transformed into the MEBot body reference frame {B}.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Sivakanthan

Castagno

Candiotti

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Common electric powered wheelchairs cannot safely negotiate architectural barriers (i.e., curbs) which could injure the user and damage the wheelchair. Robotic wheelchairs have been developed to address this issue; however, proper alignment performed by the user is needed prior to negotiating curbs. Users with physical and/or sensory impairments may find it challenging to negotiate such barriers. Hence, a Curb Recognition and Negotiation (CRN) system was developed to increase user’s speed and safety when negotiating a curb. This article describes the CRN system which combines an existing curb negotiation application of a mobility enhancement robot (MEBot) and a plane extraction algorithm called Polylidar3D to recognize curb characteristics and automatically approach and negotiate curbs. The accuracy and reliability of the CRN system were evaluated to detect an engineered curb with known height and 15 starting positions in controlled conditions. The CRN system successfully recognized curbs at 14 out of 15 starting positions and correctly determined the height and distance for the MEBot to travel towards the curb. While the MEBot curb alignment was 1.5 ± 4.4°, the curb ascending was executed safely. The findings provide support for the implementation of a robotic wheelchair to increase speed and reduce human error when negotiating curbs and improve accessibility.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Sivakanthan

Castagno

Candiotti

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…Extracting planes from point clouds is not a new idea [5]- [7]. One general strategy is to search for best-fit planes to the point cloud, which can be accomplished via RANSAC [5], [8] or the Hough transform [6], [9].…”

Section: Related Workmentioning

confidence: 99%

“…An alternative method for normal estimation is meshing the point cloud and computing normals for each triangle or polygon in the mesh. Polylidar3D [7] introduced an algorithm for fast non-convex polygon extraction from depth sensor data which relies on triangulation and region growing. One can also use meshes to construct lightweight scene representations, as in [11].…”

Section: Related Workmentioning

confidence: 99%

PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments

Dai¹,

Lund²,

Gao³

2022

Preprint

View full text Add to dashboard Cite

LiDAR sensors are a powerful tool for robot simultaneous localization and mapping (SLAM) in unknown environments, but the raw point clouds they produce are dense, computationally expensive to store, and unsuited for direct use by downstream autonomy tasks, such as motion planning. For integration with motion planning, it is desirable for SLAM pipelines to generate lightweight geometric map representations. Such representations are also particularly wellsuited for man-made environments, which can often be viewed as a so-called "Manhattan world" built on a Cartesian grid. In this work we present a 3D LiDAR SLAM algorithm for Manhattan world environments which extracts planar features from point clouds to achieve lightweight, real-time localization and mapping. Our approach generates plane-based maps which occupy significantly less memory than their point cloud equivalents, and are suited towards fast collision checking for motion planning. By leveraging the Manhattan world assumption, we target extraction of orthogonal planes to generate maps which are more structured and organized than those of existing plane-based LiDAR SLAM approaches. We demonstrate our approach in the high-fidelity AirSim simulator and in realworld experiments with a ground rover equipped with a Velodyne LiDAR. For both cases, we are able to generate high quality maps and trajectory estimates at a rate matching the sensor rate of 10 Hz.

show abstract

“…Their quantitative evaluation is based on the orientation angle of the object and the results show that representation using polyline is closer to the ground truth than the cuboid representation. A complex representation based on polygons is proposed in [ 36 ], by modelling the 3-D points cloud as a polygonal (triangular) mesh, with potential applications for aerial depth images, traffic scenes, and indoor environments.…”

Section: Related Workmentioning

confidence: 99%

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Dulău

Oniga

2021

Sensors

View full text Add to dashboard Cite

In this paper, we propose an obstacle detection approach that uses a facet-based obstacle representation. The approach has three main steps: ground point detection, clustering of obstacle points, and facet extraction. Measurements from a 64-layer LiDAR are used as input. First, ground points are detected and eliminated in order to select obstacle points and create object instances. To determine the objects, obstacle points are grouped using a channel-based clustering approach. For each object instance, its contour is extracted and, using an RANSAC-based approach, the obstacle facets are selected. For each processing stage, optimizations are proposed in order to obtain a better runtime. For the evaluation, we compare our proposed approach with an existing approach, using the KITTI benchmark dataset. The proposed approach has similar or better results for some obstacle categories but a lower computational complexity.

show abstract

Polylidar3D-Fast Polygon Extraction from 3D Data

Cited by 8 publications

References 50 publications

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

Automated Curb Recognition and Negotiation for Robotic Wheelchairs

PlaneSLAM: Plane-based LiDAR SLAM for Motion Planning in Structured 3D Environments

Obstacle Detection Using a Facet-Based Representation from 3-D LiDAR Measurements

Contact Info

Product

Resources

About