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Register a new userOneShot Global Localization: Instant LiDAR-Visual Pose Estimation
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Added by
Sebastian Ratz
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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Globally localizing in a given map is a crucial ability for robots to perform a wide range of autonomous navigation tasks. This paper presents OneShot - a global localization algorithm that uses only a single 3D LiDAR scan at a time, while outperforming approaches based on integrating a sequence of point clouds. Our approach, which does not require the robot to move, relies on learning-based descriptors of point cloud segments and computes the full 6 degree-of-freedom pose in a map. The segments are extracted from the current LiDAR scan and are matched against a database using the computed descriptors. Candidate matches are then verified with a geometric consistency test. We additionally present a strategy to further improve the performance of the segment descriptors by augmenting them with visual information provided by a camera. For this purpose, a custom-tailored neural network architecture is proposed. We demonstrate that our LiDAR-only approach outperforms a state-of-the-art baseline on a sequence of the KITTI dataset and also evaluate its performance on the challenging NCLT dataset. Finally, we show that fusing in visual information boosts segment retrieval rates by up to 26% compared to LiDAR-only description.
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In this paper, we present a novel end-to-end learning-based LiDAR relocalization framework, termed PointLoc, which infers 6-DoF poses directly using only a single point cloud as input, without requiring a pre-built map. Compared to RGB image-based relocalization, LiDAR frames can provide rich and robust geometric information about a scene. However, LiDAR point clouds are unordered and unstructured making it difficult to apply traditional deep learning regression models for this task. We address this issue by proposing a novel PointNet-style architecture with self-attention to efficiently estimate 6-DoF poses from 360{deg} LiDAR input frames.Extensive experiments on recently released challenging Oxford Radar RobotCar dataset and real-world robot experiments demonstrate that the proposedmethod can achieve accurate relocalization performance.
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Radar and lidar, provided by two different range sensors, each has pros and cons of various perception tasks on mobile robots or autonomous driving. In this paper, a Monte Carlo system is used to localize the robot with a rotating radar sensor on 2D lidar maps. We first train a conditional generative adversarial network to transfer raw radar data to lidar data, and achieve reliable radar points from generator. Then an efficient radar odometry is included in the Monte Carlo system. Combining the initial guess from odometry, a measurement model is proposed to match the radar data and prior lidar maps for final 2D positioning. We demonstrate the effectiveness of the proposed localization framework on the public multi-session dataset. The experimental results show that our system can achieve high accuracy for long-term localization in outdoor scenes.
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