Multi-modal curb detection and filtering
- URL: http://arxiv.org/abs/2205.07096v1
- Date: Sat, 14 May 2022 17:03:41 GMT
- Title: Multi-modal curb detection and filtering
- Authors: Sandipan Das, Navid Mahabadi, Saikat Chatterjee, Maurice Fallon
- Abstract summary: We propose a robust curb detection and filtering technique based on the fusion of camera semantics and dense lidar point clouds.
The proposed system has proven capable of detecting curbs of any orientation in complex urban road scenarios.
- Score: 11.74956489227383
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Reliable knowledge of road boundaries is critical for autonomous vehicle
navigation. We propose a robust curb detection and filtering technique based on
the fusion of camera semantics and dense lidar point clouds. The lidar point
clouds are collected by fusing multiple lidars for robust feature detection.
The camera semantics are based on a modified EfficientNet architecture which is
trained with labeled data collected from onboard fisheye cameras. The point
clouds are associated with the closest curb segment with $L_2$-norm analysis
after projecting into the image space with the fisheye model projection. Next,
the selected points are clustered using unsupervised density-based spatial
clustering to detect different curb regions. As new curb points are detected in
consecutive frames they are associated with the existing curb clusters using
temporal reachability constraints. If no reachability constraints are found a
new curb cluster is formed from these new points. This ensures we can detect
multiple curbs present in road segments consisting of multiple lanes if they
are in the sensors' field of view. Finally, Delaunay filtering is applied for
outlier removal and its performance is compared to traditional RANSAC-based
filtering. An objective evaluation of the proposed solution is done using a
high-definition map containing ground truth curb points obtained from a
commercial map supplier. The proposed system has proven capable of detecting
curbs of any orientation in complex urban road scenarios comprising straight
roads, curved roads, and intersections with traffic isles.
Related papers
- CurbNet: Curb Detection Framework Based on LiDAR Point Cloud Segmentation [7.451629109566809]
This paper introduces CurbNet, a novel framework for curb detection utilizing point cloud segmentation.
We have developed the 3D-Curb dataset based on Semantic KITTI, currently the largest and most diverse collection of curb point clouds.
To tackle the challenges posed by the uneven distribution of curb features on the xy-plane and their dependence on high-frequency features along the z-axis, we introduce the Multi-Scale and Channel Attention (MSCA) module.
arXiv Detail & Related papers (2024-03-25T14:13:09Z) - RCLane: Relay Chain Prediction for Lane Detection [76.62424079494285]
We present a new method for lane detection based on relay chain prediction.
Our strategy allows us to establish new state-of-the-art on four major benchmarks including TuSimple, CULane, CurveLanes and LLAMAS.
arXiv Detail & Related papers (2022-07-19T16:48:39Z) - SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and
Interaction Space Graph Reasoning for Autonomous Driving [64.10636296274168]
Road extraction is an essential step in building autonomous navigation systems.
Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image.
We propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps.
arXiv Detail & Related papers (2021-09-16T03:52:17Z) - CP-loss: Connectivity-preserving Loss for Road Curb Detection in
Autonomous Driving with Aerial Images [10.300623192980753]
Road curb detection is important for autonomous driving.
Most of the current methods detect road curbs online using vehicle-mounted sensors, such as cameras or 3-D Lidars.
In this paper, we detect road curbs offline using high-resolution aerial images.
arXiv Detail & Related papers (2021-07-26T01:36:58Z) - DeepI2P: Image-to-Point Cloud Registration via Deep Classification [71.3121124994105]
DeepI2P is a novel approach for cross-modality registration between an image and a point cloud.
Our method estimates the relative rigid transformation between the coordinate frames of the camera and Lidar.
We circumvent the difficulty by converting the registration problem into a classification and inverse camera projection optimization problem.
arXiv Detail & Related papers (2021-04-08T04:27:32Z) - Online Clustering-based Multi-Camera Vehicle Tracking in Scenarios with
overlapping FOVs [2.6365690297272617]
Multi-Target Multi-Camera (MTMC) vehicle tracking is an essential task of visual traffic monitoring.
We present a new low-latency online approach for MTMC tracking in scenarios with partially overlapping fields of view.
arXiv Detail & Related papers (2021-02-08T09:55:55Z) - Towards Autonomous Driving: a Multi-Modal 360$^{\circ}$ Perception
Proposal [87.11988786121447]
This paper presents a framework for 3D object detection and tracking for autonomous vehicles.
The solution, based on a novel sensor fusion configuration, provides accurate and reliable road environment detection.
A variety of tests of the system, deployed in an autonomous vehicle, have successfully assessed the suitability of the proposed perception stack.
arXiv Detail & Related papers (2020-08-21T20:36:21Z) - Lane Detection Model Based on Spatio-Temporal Network With Double
Convolutional Gated Recurrent Units [11.968518335236787]
Lane detection will remain an open problem for some time to come.
A-temporal network with double Conal Gated Recurrent Units (ConvGRUs) proposed to address lane detection in challenging scenes.
Our model can outperform the state-of-the-art lane detection models.
arXiv Detail & Related papers (2020-08-10T06:50:48Z) - Tracking Road Users using Constraint Programming [79.32806233778511]
We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem.
Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.
arXiv Detail & Related papers (2020-03-10T00:04:32Z) - Road Curb Detection and Localization with Monocular Forward-view Vehicle
Camera [74.45649274085447]
We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens.
Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
arXiv Detail & Related papers (2020-02-28T00:24:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.