50 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
NEPTUNE: Non-Entangling Planning for Multiple Tethered Unmanned Vehicles
Despite recent progress on trajectory planning of multiple robots and path
planning of a single tethered robot, planning of multiple tethered robots to
reach their individual targets without entanglements remains a challenging
problem. In this paper, we present a complete approach to address this problem.
Firstly, we propose a multi-robot tether-aware representation of homotopy,
using which we can efficiently evaluate the feasibility and safety of a
potential path in terms of (1) the cable length required to reach a target
following the path, and (2) the risk of entanglements with the cables of other
robots. Then, the proposed representation is applied in a decentralized and
online planning framework that includes a graph-based kinodynamic trajectory
finder and an optimization-based trajectory refinement, to generate
entanglement-free, collision-free and dynamically feasible trajectories. The
efficiency of the proposed homotopy representation is compared against existing
single and multiple tethered robot planning approaches. Simulations with up to
8 UAVs show the effectiveness of the approach in entanglement prevention and
its real-time capabilities. Flight experiments using 3 tethered UAVs verify the
practicality of the presented approach.Comment: Accepted for publication in IEEE Transaction on Robotic
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
SPINS: Structure Priors aided Inertial Navigation System
Although Simultaneous Localization and Mapping (SLAM) has been an active
research topic for decades, current state-of-the-art methods still suffer from
instability or inaccuracy due to feature insufficiency or its inherent
estimation drift, in many civilian environments. To resolve these issues, we
propose a navigation system combing the SLAM and prior-map-based localization.
Specifically, we consider additional integration of line and plane features,
which are ubiquitous and more structurally salient in civilian environments,
into the SLAM to ensure feature sufficiency and localization robustness. More
importantly, we incorporate general prior map information into the SLAM to
restrain its drift and improve the accuracy. To avoid rigorous association
between prior information and local observations, we parameterize the prior
knowledge as low dimensional structural priors defined as relative
distances/angles between different geometric primitives. The localization is
formulated as a graph-based optimization problem that contains
sliding-window-based variables and factors, including IMU, heterogeneous
features, and structure priors. We also derive the analytical expressions of
Jacobians of different factors to avoid the automatic differentiation overhead.
To further alleviate the computation burden of incorporating structural prior
factors, a selection mechanism is adopted based on the so-called information
gain to incorporate only the most effective structure priors in the graph
optimization. Finally, the proposed framework is extensively tested on
synthetic data, public datasets, and, more importantly, on the real UAV flight
data obtained from a building inspection task. The results show that the
proposed scheme can effectively improve the accuracy and robustness of
localization for autonomous robots in civilian applications.Comment: 14 pages, 14 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