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Found 8 papers, 4 papers with code
SLAM auto-complete: completing a robot map using an emergency map
Experiments in an office environment show that we can handle up to 70% of wrong correspondences and still get the expected result.
Robotics
A method to segment maps from different modalities using free space layout -- MAORIS : MAp Of RIpples Segmentation
We present a method for segmenting maps from different modalities, focusing on robot built maps and hand-drawn sketch maps, and show better results than state of the art for both types.
Robotics
Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
More importantly, the Gaussian method (i. e. deep probabilistic localisation) and non-Gaussian method (i. e. MCL) can be integrated naturally via importance sampling.
Robust Frequency-Based Structure Extraction
Our experiments demonstrate that (1) the application of ROSE for decluttering can substantially improve structural feature retrieval (e. g., walls) in cluttered environments, (2) ROSE can successfully distinguish between clutter and structure in the map even with substantial amount of noise and (3) ROSE can numerically assess the amount of structure in the map.
Robotics Functional Analysis
CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation.
Ranked #1 on
Radar odometry
on Oxford Radar RobotCar Dataset
Learning Extrinsic Dexterity with Parameterized Manipulation Primitives
We solve this problem by learning a sequence of actions that utilize the environment to change the object's pose.
High-Fidelity SLAM Using Gaussian Splatting with Rendering-Guided Densification and Regularized Optimization
We propose a dense RGBD SLAM system based on 3D Gaussian Splatting that provides metrically accurate pose tracking and visually realistic reconstruction.
Human Detection from 4D Radar Data in Low-Visibility Field Conditions
The CNN is trained to distinguish between the background and person classes based on a series of 2D projections of the 4D radar data that include the elevation, azimuth, range, and Doppler velocity dimensions.
