InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method

• URL: http://arxiv.org/abs/2505.00512v3
• Date: Wed, 16 Jul 2025 05:37:01 GMT
• Title: InterLoc: LiDAR-based Intersection Localization using Road Segmentation with Automated Evaluation Method
• Authors: Nguyen Hoang Khoi Tran, Julie Stephany Berrio, Mao Shan, Zhenxing Ming, Stewart Worrall,
• Abstract summary: We present a novel LiDAR-based method for online vehicle-centric intersection localization.<n>We detect intersection candidates in a bird's eye view (BEV) representation formed by concatenating semantic road scans.<n>Experiments on the Semantic KITTITI dataset show that our method outperforms the latest learning-based baseline in accuracy and reliability.
• Score: 10.561470037080177
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online localization of road intersections is beneficial for autonomous vehicle localization, mapping and motion planning. Intersections offer strong landmarks for correcting vehicle pose estimation, anchoring new sensor data in up-to-date maps, and guiding vehicle routing in road network graphs. Despite this importance, intersection localization has not been widely studied, with existing methods either ignoring the rich semantic information already computed onboard or relying on scarce, hand-labeled intersection datasets. To close this gap, we present a novel LiDAR-based method for online vehicle-centric intersection localization. We detect the intersection candidates in a bird's eye view (BEV) representation formed by concatenating a sequence of semantic road scans. We then refine these candidates by analyzing the intersecting road branches and adjusting the intersection center point in a least-squares formulation. For evaluation, we introduce an automated pipeline that pairs localized intersection points with OpenStreetMap (OSM) intersection nodes using precise GNSS/INS ground-truth poses. Experiments on the SemanticKITTI dataset show that our method outperforms the latest learning-based baseline in accuracy and reliability. Sensitivity tests demonstrate the method's robustness to challenging segmentation errors, highlighting its applicability in the real world.

Related papers

• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Homography Guided Temporal Fusion for Road Line and Marking Segmentation [73.47092021519245]
  Road lines and markings are frequently occluded in the presence of moving vehicles, shadow, and glare. We propose a Homography Guided Fusion (HomoFusion) module to exploit temporally-adjacent video frames for complementary cues. We show that exploiting available camera intrinsic data and ground plane assumption for cross-frame correspondence can lead to a light-weight network with significantly improved performances in speed and accuracy.
  arXiv  Detail & Related papers  (2024-04-11T10:26:40Z)
• Improved LiDAR Odometry and Mapping using Deep Semantic Segmentation and Novel Outliers Detection [1.0334138809056097]
  We propose a novel framework for real-time LiDAR odometry and mapping based on LOAM architecture for fast moving platforms. Our framework utilizes semantic information produced by a deep learning model to improve point-to-line and point-to-plane matching. We study the effect of improving the matching process on the robustness of LiDAR odometry against high speed motion.
  arXiv  Detail & Related papers  (2024-03-05T16:53:24Z)
• Road Graph Generator: Mapping roads at construction sites from GPS data [0.0]
  We propose a new method for inferring roads from GPS trajectories to map construction sites.<n>This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery.<n>Our proposed method first identifies intersections in the road network that serve as critical decision points, and then connects them with edges to produce a graph.
  arXiv  Detail & Related papers  (2024-02-15T12:53:25Z)
• Improving Online Lane Graph Extraction by Object-Lane Clustering [106.71926896061686]
  We propose an architecture and loss formulation to improve the accuracy of local lane graph estimates. The proposed method learns to assign the objects to centerlines by considering the centerlines as cluster centers. We show that our method can achieve significant performance improvements by using the outputs of existing 3D object detection methods.
  arXiv  Detail & Related papers  (2023-07-20T15:21:28Z)
• Automated Static Camera Calibration with Intelligent Vehicles [58.908194559319405]
  We present a robust calibration method for automated geo-referenced camera calibration. Our method requires a calibration vehicle equipped with a combined filtering/RTK receiver and an inertial measurement unit (IMU) for self-localization. Our method does not require any human interaction with the information recorded by both the infrastructure and the vehicle.
  arXiv  Detail & Related papers  (2023-04-21T08:50:52Z)
• Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection [33.78080060234557]
  Nearest-Neighbors approaches have been shown to work well in object-centric data domains. We show that nearest-neighbor approaches also yield state-of-the-art results on dense novelty detection in complex driving scenes.
  arXiv  Detail & Related papers  (2022-11-12T13:32:19Z)
• Improving Fuzzy-Logic based Map-Matching Method with Trajectory Stay-Point Detection [3.093890460224435]
  Most GPS trajectory datasets include stay-points irregularity, which makes map-matching algorithms mismatch trajectories to irrelevant streets. We cluster stay-points in a trajectory dataset with DBSCAN and eliminate redundant data to improve the efficiency of the map-matching algorithm. Our approach yields 27.39% data size reduction and 8.9% processing time reduction with the same accurate results as the previous fuzzy-logic based map-matching approach.
  arXiv  Detail & Related papers  (2022-08-04T20:41:13Z)
• Semi-supervised Domain Adaptive Structure Learning [72.01544419893628]
  Semi-supervised domain adaptation (SSDA) is a challenging problem requiring methods to overcome both 1) overfitting towards poorly annotated data and 2) distribution shift across domains. We introduce an adaptive structure learning method to regularize the cooperation of SSL and DA.
  arXiv  Detail & Related papers  (2021-12-12T06:11:16Z)
• Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations [68.0524382279567]
  We present a robust learning algorithm to detect and handle collisions in 3D deforming meshes. Our approach outperforms supervised learning methods and achieves $93.8-98.1%$ accuracy.
  arXiv  Detail & Related papers  (2021-10-08T04:08:31Z)
• PSE-Match: A Viewpoint-free Place Recognition Method with Parallel Semantic Embedding [9.265785042748158]
  PSE-Match is a viewpoint-free place recognition method based on parallel semantic analysis of isolated semantic attributes from 3D point-cloud models. PSE-Match incorporates a divergence place learning network to capture different semantic attributes parallelly through the spherical harmonics domain.
  arXiv  Detail & Related papers  (2021-08-01T22:16:40Z)
• Open-set Intersection Intention Prediction for Autonomous Driving [9.494867137826397]
  We formulate the prediction of intention at intersections as an open-set prediction problem. We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation. We use two MAAMs (mutually auxiliary attention module) to predict a target that best matches intersection elements in map-centric feature space.
  arXiv  Detail & Related papers  (2021-02-27T06:38:26Z)
• Fusion of neural networks, for LIDAR-based evidential road mapping [3.065376455397363]
  We introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. We thus secondly present an evidential road mapping algorithm, that fuses consecutive road detection results.
  arXiv  Detail & Related papers  (2021-02-05T18:14:36Z)
• Graph Regularized Autoencoder and its Application in Unsupervised Anomaly Detection [42.86693635734333]
  We propose to use a minimum spanning tree (MST) to approximate the local neighborhood structure and generate structure-preserving distances among data points. We develop a new graph regularized autoencoder, which outperforms a wide range of alternative methods over 20 benchmark anomaly detection datasets.
  arXiv  Detail & Related papers  (2020-10-29T21:17:41Z)
• Radar-based Dynamic Occupancy Grid Mapping and Object Detection [55.74894405714851]
  In recent years, the classical occupancy grid map approach has been extended to dynamic occupancy grid maps. This paper presents the further development of a previous approach. The data of multiple radar sensors are fused, and a grid-based object tracking and mapping method is applied.
  arXiv  Detail & Related papers  (2020-08-09T09:26:30Z)
• UC-Net: Uncertainty Inspired RGB-D Saliency Detection via Conditional Variational Autoencoders [81.5490760424213]
  We propose the first framework (UCNet) to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose probabilistic RGB-D saliency detection network.
  arXiv  Detail & Related papers  (2020-04-13T04:12:59Z)
• Robust 6D Object Pose Estimation by Learning RGB-D Features [59.580366107770764]
  We propose a novel discrete-continuous formulation for rotation regression to resolve this local-optimum problem. We uniformly sample rotation anchors in SO(3), and predict a constrained deviation from each anchor to the target, as well as uncertainty scores for selecting the best prediction. Experiments on two benchmarks: LINEMOD and YCB-Video, show that the proposed method outperforms state-of-the-art approaches.
  arXiv  Detail & Related papers  (2020-02-29T06:24:55Z)
• Key Points Estimation and Point Instance Segmentation Approach for Lane Detection [65.37887088194022]
  We propose a traffic line detection method called Point Instance Network (PINet) The PINet includes several stacked hourglass networks that are trained simultaneously. The PINet achieves competitive accuracy and false positive on the TuSimple and Culane datasets.
  arXiv  Detail & Related papers  (2020-02-16T15:51:30Z)

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.