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Found 27 papers, 9 papers with code
EffLoc: Lightweight Vision Transformer for Efficient 6-DOF Camera Relocalization
Camera relocalization is pivotal in computer vision, with applications in AR, drones, robotics, and autonomous driving.
DK-SLAM: Monocular Visual SLAM with Deep Keypoints Adaptive Learning, Tracking and Loop-Closing
Unreliable feature extraction and matching in handcrafted features undermine the performance of visual SLAM in complex real-world scenarios.
ReLoc-PDR: Visual Relocalization Enhanced Pedestrian Dead Reckoning via Graph Optimization
Accurately and reliably positioning pedestrians in satellite-denied conditions remains a significant challenge.
Drone-NeRF: Efficient NeRF Based 3D Scene Reconstruction for Large-Scale Drone Survey
In this work, we propose the Drone-NeRF framework to enhance the efficient reconstruction of unbounded large-scale scenes suited for drone oblique photography using Neural Radiance Fields (NeRF).
Deep Learning for Visual Localization and Mapping: A Survey
Deep learning based localization and mapping approaches have recently emerged as a new research direction and receive significant attentions from both industry and academia.
Simultaneous Localization and Mapping
Visual Localization
+1
Deep Learning for Inertial Positioning: A Survey
Inertial sensors are widely utilized in smartphones, drones, robots, and IoT devices, playing a crucial role in enabling ubiquitous and reliable localization.
SelfOdom: Self-supervised Egomotion and Depth Learning via Bi-directional Coarse-to-Fine Scale Recovery
To address this, we propose SelfOdom, a self-supervised dual-network framework that can robustly and consistently learn and generate pose and depth estimates in global scale from monocular images.
EMA-VIO: Deep Visual-Inertial Odometry with External Memory Attention
Accurate and robust localization is a fundamental need for mobile agents.
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction
Self-supervised depth learning from monocular images normally relies on the 2D pixel-wise photometric relation between temporally adjacent image frames.
P2-Net: Joint Description and Detection of Local Features for Pixel and Point Matching
Accurately describing and detecting 2D and 3D keypoints is crucial to establishing correspondences across images and point clouds.
A Survey on Deep Learning for Localization and Mapping: Towards the Age of Spatial Machine Intelligence
Deep learning based localization and mapping has recently attracted significant attention.
VMLoc: Variational Fusion For Learning-Based Multimodal Camera Localization
We conjecture that this is because of the naive approaches to feature space fusion through summation or concatenation which do not take into account the different strengths of each modality.
PointLoc: Deep Pose Regressor for LiDAR Point Cloud Localization
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.
Robotics
Deep Learning based Pedestrian Inertial Navigation: Methods, Dataset and On-Device Inference
Modern inertial measurements units (IMUs) are small, cheap, energy efficient, and widely employed in smart devices and mobile robots.
Learning Selective Sensor Fusion for States Estimation
By integrating the observations from different sensors, these mobile agents are able to perceive the environment and estimate system states, e. g. locations and orientations.
See Through Smoke: Robust Indoor Mapping with Low-cost mmWave Radar
This paper presents the design, implementation and evaluation of milliMap, a single-chip millimetre wave (mmWave) radar based indoor mapping system targetted towards low-visibility environments to assist in emergency response.
DeepPCO: End-to-End Point Cloud Odometry through Deep Parallel Neural Network
There is considerable work in the area of visual odometry (VO), and recent advances in deep learning have brought novel approaches to VO, which directly learn salient features from raw images.
DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination
The hallucination network is taught to predict fake visual features from thermal images by using Huber loss.
AtLoc: Attention Guided Camera Localization
Deep learning has achieved impressive results in camera localization, but current single-image techniques typically suffer from a lack of robustness, leading to large outliers.
Ranked #2 on
Visual Localization
on Oxford RobotCar Full
Autonomous Learning for Face Recognition in the Wild via Ambient Wireless Cues
Inspired by the fact that most people carry smart wireless devices with them, e. g. smartphones, we propose to use this wireless identifier as a supervisory label.
DynaNet: Neural Kalman Dynamical Model for Motion Estimation and Prediction
In addition we show how DynaNet can indicate failures through investigation of properties such as the rate of innovation (Kalman Gain).
mID: Tracking and Identifying People with Millimeter Wave Radar
The key to offering personalised services in smart spaces is knowing where a particular person is with a high degree of accuracy.
Selective Sensor Fusion for Neural Visual-Inertial Odometry
Deep learning approaches for Visual-Inertial Odometry (VIO) have proven successful, but they rarely focus on incorporating robust fusion strategies for dealing with imperfect input sensory data.
Learning with Stochastic Guidance for Navigation
Due to the sparse rewards and high degree of environment variation, reinforcement learning approaches such as Deep Deterministic Policy Gradient (DDPG) are plagued by issues of high variance when applied in complex real world environments.
Robotics
Transferring Physical Motion Between Domains for Neural Inertial Tracking
Inertial information processing plays a pivotal role in ego-motion awareness for mobile agents, as inertial measurements are entirely egocentric and not environment dependent.
OxIOD: The Dataset for Deep Inertial Odometry
Advances in micro-electro-mechanical (MEMS) techniques enable inertial measurements units (IMUs) to be small, cheap, energy efficient, and widely used in smartphones, robots, and drones.
IONet: Learning to Cure the Curse of Drift in Inertial Odometry
Inertial sensors play a pivotal role in indoor localization, which in turn lays the foundation for pervasive personal applications.
