44 research outputs found
LIO-GVM: an Accurate, Tightly-Coupled Lidar-Inertial Odometry with Gaussian Voxel Map
This letter presents an accurate and robust Lidar Inertial Odometry
framework. We fuse LiDAR scans with IMU data using a tightly-coupled iterative
error state Kalman filter for robust and fast localization. To achieve robust
correspondence matching, we represent the points as a set of Gaussian
distributions and evaluate the divergence in variance for outlier rejection.
Based on the fitted distributions, a new residual metric is proposed for the
filter-based Lidar inertial odometry, which demonstrates an improvement from
merely quantifying distance to incorporating variance disparity, further
enriching the comprehensiveness and accuracy of the residual metric. Due to the
strategic design of the residual metric, we propose a simple yet effective
voxel-solely mapping scheme, which only necessities the maintenance of one
centroid and one covariance matrix for each voxel. Experiments on different
datasets demonstrate the robustness and accuracy of our framework for various
data inputs and environments. To the benefit of the robotics society, we open
source the code at https://github.com/Ji1Xingyu/lio_gvm
SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping
While feature association to a global map has significant benefits, to keep
the computations from growing exponentially, most lidar-based odometry and
mapping methods opt to associate features with local maps at one voxel scale.
Taking advantage of the fact that surfels (surface elements) at different voxel
scales can be organized in a tree-like structure, we propose an octree-based
global map of multi-scale surfels that can be updated incrementally. This
alleviates the need for recalculating, for example, a k-d tree of the whole map
repeatedly. The system can also take input from a single or a number of
sensors, reinforcing the robustness in degenerate cases. We also propose a
point-to-surfel (PTS) association scheme, continuous-time optimization on PTS
and IMU preintegration factors, along with loop closure and bundle adjustment,
making a complete framework for Lidar-Inertial continuous-time odometry and
mapping. Experiments on public and in-house datasets demonstrate the advantages
of our system compared to other state-of-the-art methods. To benefit the
community, we release the source code and dataset at
https://github.com/brytsknguyen/slict
Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning
One-shot LiDAR localization refers to the ability to estimate the robot pose
from one single point cloud, which yields significant advantages in
initialization and relocalization processes. In the point cloud domain, the
topic has been extensively studied as a global descriptor retrieval (i.e., loop
closure detection) and pose refinement (i.e., point cloud registration) problem
both in isolation or combined. However, few have explicitly considered the
relationship between candidate retrieval and correspondence generation in pose
estimation, leaving them brittle to substructure ambiguities. To this end, we
propose a hierarchical one-shot localization algorithm called Outram that
leverages substructures of 3D scene graphs for locally consistent
correspondence searching and global substructure-wise outlier pruning. Such a
hierarchical process couples the feature retrieval and the correspondence
extraction to resolve the substructure ambiguities by conducting a
local-to-global consistency refinement. We demonstrate the capability of Outram
in a variety of scenarios in multiple large-scale outdoor datasets. Our
implementation is open-sourced: https://github.com/Pamphlett/Outram.Comment: 8 pages, 5 figure
MM-Fi: Multi-Modal Non-Intrusive 4D Human Dataset for Versatile Wireless Sensing
4D human perception plays an essential role in a myriad of applications, such
as home automation and metaverse avatar simulation. However, existing solutions
which mainly rely on cameras and wearable devices are either privacy intrusive
or inconvenient to use. To address these issues, wireless sensing has emerged
as a promising alternative, leveraging LiDAR, mmWave radar, and WiFi signals
for device-free human sensing. In this paper, we propose MM-Fi, the first
multi-modal non-intrusive 4D human dataset with 27 daily or rehabilitation
action categories, to bridge the gap between wireless sensing and high-level
human perception tasks. MM-Fi consists of over 320k synchronized frames of five
modalities from 40 human subjects. Various annotations are provided to support
potential sensing tasks, e.g., human pose estimation and action recognition.
Extensive experiments have been conducted to compare the sensing capacity of
each or several modalities in terms of multiple tasks. We envision that MM-Fi
can contribute to wireless sensing research with respect to action recognition,
human pose estimation, multi-modal learning, cross-modal supervision, and
interdisciplinary healthcare research.Comment: The paper has been accepted by NeurIPS 2023 Datasets and Benchmarks
Track. Project page: https://ntu-aiot-lab.github.io/mm-f