5 research outputs found
LocNet: Global localization in 3D point clouds for mobile vehicles
Global localization in 3D point clouds is a challenging problem of estimating
the pose of vehicles without any prior knowledge. In this paper, a solution to
this problem is presented by achieving place recognition and metric pose
estimation in the global prior map. Specifically, we present a semi-handcrafted
representation learning method for LiDAR point clouds using siamese LocNets,
which states the place recognition problem to a similarity modeling problem.
With the final learned representations by LocNet, a global localization
framework with range-only observations is proposed. To demonstrate the
performance and effectiveness of our global localization system, KITTI dataset
is employed for comparison with other algorithms, and also on our long-time
multi-session datasets for evaluation. The result shows that our system can
achieve high accuracy.Comment: 6 pages, IV 2018 accepte
OREOS: Oriented Recognition of 3D Point Clouds in Outdoor Scenarios
We introduce a novel method for oriented place recognition with 3D LiDAR
scans. A Convolutional Neural Network is trained to extract compact descriptors
from single 3D LiDAR scans. These can be used both to retrieve near-by place
candidates from a map, and to estimate the yaw discrepancy needed for
bootstrapping local registration methods. We employ a triplet loss function for
training and use a hard-negative mining strategy to further increase the
performance of our descriptor extractor. In an evaluation on the NCLT and KITTI
datasets, we demonstrate that our method outperforms related state-of-the-art
approaches based on both data-driven and handcrafted data representation in
challenging long-term outdoor conditions