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Found 13 papers, 8 papers with code
Leveraging BEV Representation for 360-degree Visual Place Recognition
In addition, the image and point cloud cues can be easily stated in the same coordinates, which benefits sensor fusion for place recognition.
DeepRING: Learning Roto-translation Invariant Representation for LiDAR based Place Recognition
In recent years, deep learning brings improvements to place recognition by learnable feature extraction.
RING++: Roto-translation Invariant Gram for Global Localization on a Sparse Scan Map
In addition, we derive sufficient conditions of feature extractors for the representation preserving the roto-translation invariance, making RING++ a framework applicable to generic multi-channel features.
DPCN++: Differentiable Phase Correlation Network for Versatile Pose Registration
Next, the rotation, scale, and translation are independently and efficiently estimated in the spectrum step-by-step using the DPC solver.
Translation Invariant Global Estimation of Heading Angle Using Sinogram of LiDAR Point Cloud
Global point cloud registration is an essential module for localization, of which the main difficulty exists in estimating the rotation globally without initial value.
Learning Interpretable BEV Based VIO without Deep Neural Networks
Specifically, we first adopt Unscented Kalman Filter as a differentiable layer to predict the pitch and roll, where the covariance matrices of noise are learned to filter out the noise of the IMU raw data.
Learn to Differ: Sim2Real Small Defection Segmentation Network
In this paper, we propose the network SSDS that learns a way of distinguishing small defections between two images regardless of the context, so that the network can be trained once for all.
Radar-to-Lidar: Heterogeneous Place Recognition via Joint Learning
Place recognition is critical for both offline mapping and online localization.
GEM: Online Globally consistent dense elevation mapping for unstructured terrain
As a result, this deformable global dense map representation is able to keep the global consistency online.
CORAL: Colored structural representation for bi-modal place recognition
In this way, we fuse the structural features and visual features in the consistent bird-eye view frame, yielding a semantic representation, namely CORAL.
PREGAN: Pose Randomization and Estimation for Weakly Paired Image Style Translation
Utilizing the trained model under different conditions without data annotation is attractive for robot applications.
DiSCO: Differentiable Scan Context with Orientation
In this paper, we propose a LiDAR-based place recognition method, named Differentiable Scan Context with Orientation (DiSCO), which simultaneously finds the scan at a similar place and estimates their relative orientation.
Deep Phase Correlation for End-to-End Heterogeneous Sensor Measurements Matching
The crucial step for localization is to match the current observation to the map.
