Search Results for author:
Found 15 papers, 2 papers with code
BezierFormer: A Unified Architecture for 2D and 3D Lane Detection
B\'{e}zierFormer formulate queries as B\'{e}zier control points and incorporate a novel B\'{e}zier curve attention mechanism.
A survey on deep learning approaches for data integration in autonomous driving system
The perception module of self-driving vehicles relies on a multi-sensor system to understand its environment.
NeMO: Neural Map Growing System for Spatiotemporal Fusion in Bird's-Eye-View and BDD-Map Benchmark
We also provide a new scene-level BEV map evaluation setting along with the corresponding baseline for a more comprehensive comparison.
RIO: Rotation-equivariance supervised learning of robust inertial odometry
This paper introduces rotation-equivariance as a self-supervisor to train inertial odometry models.
Learning multiview 3D point cloud registration
We present a novel, end-to-end learnable, multiview 3D point cloud registration algorithm.
Feature-wise change detection and robust indoor positioning using RANSAC-like approach
One critical challenge is to controland improve the quality of the reference fingerprint map (RFM), which is built at the offline stage and applied for online positioning.
An iterative scheme for feature based positioning using a weighted dissimilarity measure
In the positioning stage, the weights control the contribution of each feature to the dissimilarity measure, which in turn quantifies the difference between the set of online measured features and the fingerprints stored in the RFM.
The Perfect Match: 3D Point Cloud Matching with Smoothed Densities
Our approach is sensor- and sceneagnostic because of SDV, LRF and learning highly descriptive features with fully convolutional layers.
Ranked #4 on
Point Cloud Registration
on ETH (trained on 3DMatch)
CDM: Compound dissimilarity measure and an application to fingerprinting-based positioning
This proposed compound dissimilarity measure (CDM) is applicable to quantify similarity of collections of attribute/feature pairs where not all attributes are present in all collections.
Jaccard analysis and LASSO-based feature selection for location fingerprinting with limited computational complexity
We propose an approach to reduce both computational complexity and data storage requirements for the online positioning stage of a fingerprinting-based indoor positioning system (FIPS) by introducing segmentation of the region of interest (RoI) into sub-regions, sub-region selection using a modified Jaccard index, and feature selection based on randomized least absolute shrinkage and selection operator (LASSO).
WiFi based trajectory alignment, calibration and easy site survey using smart phones and foot-mounted IMUs
Foot-mounted inertial positioning (FMIP) can face problems of inertial drifts and unknown initial states in real applications, which renders the estimated trajectories inaccurate and not obtained in a well defined coordinate system for matching trajectories of different users.
Joint Positioning and Radio Map Generation Based on Stochastic Variational Bayesian Inference for FWIPS
This SVBI based position and RSS estimation has three properties: i) being able to predict the distribution of the estimated position and RSS, ii) treating each observation of RSS at each RP as an example to learn for FbP and RM generation instead of using the whole RM as an example, and iii) requiring only one time training of the SVBI model for both localization and RSS estimation.
Adaptive Neighboring Selection Algorithm Based on Curvature Prediction in Manifold Learning
By verifying the proposed algorithm on embedding Swiss roll from R3 to R2 based on LLE and ISOMAP algorithm, the simulation results show that the proposed adaptive neighboring selection algorithm is feasible and able to find the optimal value of K, making the residual variance relatively small and better visualization of the results.
Joint Semi-supervised RSS Dimensionality Reduction and Fingerprint Based Algorithm for Indoor Localization
Another significant innovation of this paper is jointing the fingerprint based algorithm with CM-SDE algorithm to improve the localization accuracy of indoor localization.
Application of backpropagation neural networks to both stages of fingerprinting based WIPS
We propose a scheme to employ backpropagation neural networks (BPNNs) for both stages of fingerprinting-based indoor positioning using WLAN/WiFi signal strengths (FWIPS): radio map construction during the offline stage, and localization during the online stage.
