Improving vehicle localization using semantic and pole-like landmarks

Sefati, Mohsen; Daum, Maureen; Sondermann, Bjoern; Kreisköther, Kai; Kampker, Achim

doi:10.1109/ivs.2017.7995692

Cited by 90 publications

(49 citation statements)

References 23 publications

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“…For 2-D pose estimation, the authors employ on a particle filter with nearest-neighbor data association. Sefati et al [8] present a pole detector that removes the ground plane from a given point cloud, projects the remaining points onto a horizontal regular grid, clusters neighboring cells based on occupancy and height, and fits a cylinder to each cluster. Like Weng et al, Sefati et al obtain their 2-D localization estimate from a particle filter that performs nearest-neighbor data association.…”

Section: Related Workmentioning

confidence: 99%

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Schaefer

Büscher

Vertens

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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Due to their ubiquity and long-term stability, polelike objects are well suited to serve as landmarks for vehicle localization in urban environments. In this work, we present a complete mapping and long-term localization system based on pole landmarks extracted from 3-D lidar data. Our approach features a novel pole detector, a mapping module, and an online localization module, each of which are described in detail, and for which we provide an open-source implementation [1].In extensive experiments, we demonstrate that our method improves on the state of the art with respect to long-term reliability and accuracy: First, we prove reliability by tasking the system with localizing a mobile robot over the course of 15 months in an urban area based on an initial map, confronting it with constantly varying routes, differing weather conditions, seasonal changes, and construction sites. Second, we show that the proposed approach clearly outperforms a recently published method in terms of accuracy.

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Section: Related Workmentioning

confidence: 99%

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Schaefer

Büscher

Vertens

et al. 2019

2019 European Conference on Mobile Robots (ECMR)

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“…Pole like features are detected from on-board lidar [12][13] [14], stereo camera [15][16] and pole detection neural networks from monocular camera [17]. [18] showed its lidar pole detection is more precise than stereo-camera algorithm. [19] presents a graph-based localization algorithm using only tree and pillar landmarks.…”

Section: B Sparse High Level Landmarks As Features For Localization mentioning

confidence: 99%

Geographical Map Registration and Fusion of Lidar-Aerial Orthoimagery in GIS

Nebot

2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Centimeter level globally accurate and consistent maps for autonomous vehicles navigation has long been achieved by on board real-time kinematic(RTK)-GPS in open areas. However when dealing with urban environments, GPS will experience multipath and blockage in urban canyon, under bridges, inside tunnels and in underground environments. In this paper we present strategies to efficiently register local maps in geographical coordinate systems through the tactical integration of GPS and information extracted from precisely geo-referenced high resolution aerial orthogonal imagery. Dense lidar point clouds obtained from moving vehicle are projected down to horizontal plane, accurately registered and overlaid on aerial orthoimagery. Sparse, robust and long-term polelike landmarks are used as anchor points to link lidar and aerial image sensing, and constrain the spatial uncertainties of remaining lidar points that cannot be directly measured and identified. We achieved 15-75cm absolute average global accuracy using precisely geo-referenced aerial imagery as ground truth. This is valuable in enabling the fusion of ground vehicle on-board sensor features with features extracted from aerial images such as traffic and lane markings. It is also useful for cooperative sensing to have an unbiased and accurate global reference. Experimental results are presented demonstrating the accuracy and consistency of the maps when operating in large areas.

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“…For LiDAR-based localization techniques in this context, pole-like landmark [8][9] is a suitable option, but it is not common in indoor parking lots, which limits its application to an outdoor place surrounded by a certain number of pole items like trees, street lamps or traffic signs. Another shortcoming of such approach is that it needs to use multiline LiDAR to distinguish the pole items from others and extract their geometric features, considering that horizontal size of the lamps or signs is generally small and the irregular geometric features of trees limit its positioning accuracy.…”

Section: Related Workmentioning

confidence: 99%

Self-Localization of Parking Robots Using Square-Like Landmarks

Chen

et al. 2018

2018 IEEE International Conference on Robotics and Biomimetics (ROBIO)

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In this paper, we present a framework for selflocalization of parking robots in a parking lot innovatively using square-like landmarks, aiming to provide a positioning solution with low cost but high accuracy. It utilizes square structures common in parking lots such as pillars, corners or charging piles as robust landmarks and deduces the global pose of the robot in conjunction with an off-line map. The localization is performed in real-time via Particle Filter using a single line scanning LiDAR as main sensor, an odometry as secondary information sources. The system has been tested in a simulation environment built in V-REP, the result of which demonstrates its positioning accuracy below 0.20 m and a corresponding heading error below 1 • .

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Improving vehicle localization using semantic and pole-like landmarks

Cited by 90 publications

References 23 publications

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Long-Term Urban Vehicle Localization Using Pole Landmarks Extracted from 3-D Lidar Scans

Geographical Map Registration and Fusion of Lidar-Aerial Orthoimagery in GIS

Self-Localization of Parking Robots Using Square-Like Landmarks

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