Five architectural deficiencies in a representative tightly coupled IESKF-based LIO implementation are identified and corresponding modifications including IMU-rate forward propagation, direct body-frame velocity publishing, SLERP-based smoothing, dual-executor isolation, and explicit synchronization protection are introduced.
Abstract
Open-source LiDAR-inertial odometry (LIO) systems have achieved remarkable benchmark accuracy, yet current state-of-the-art implementations are primarily optimized for evaluation performance rather than the requirements of real-time closed-loop aerial control. When deployed onboard UAVs, this can introduce limitations that degrade flight performance. In this work, we identify five architectural deficiencies in a representative tightly coupled IESKF-based LIO implementation: odometry publishing tied to the LiDAR rate (10 Hz instead of the IMU's 200 Hz), missing velocity outputs, execution bottlenecks that block IMU processing, mutex contention, and synchronization race conditions. We introduce corresponding modifications including IMU-rate forward propagation, direct body-frame velocity publishing, SLERP-based smoothing, dual-executor isolation, and explicit synchronization protection. The resulting system increases odometry output from ~10 Hz to a stable 200 Hz, provides a complete Twist state at every IMU sample, and preserves continuity during transient LiDAR loss. Experiments on a Livox Mid-360 / Pixhawk 4 Mini autonomous UAV with motion-capture ground truth validate the approach. Since the underlying estimator (IESKF + ikd-Tree) remains unchanged, the proposed improvements can be directly applied to FAST-LIO2-derived implementations.
: Light Detection and Ranging (LiDAR)–Inertial Odometry (LIO), which tightly fuses complementary data from LiDAR and Inertial Measurement Units (IMUs), is a key technology for high-precision state estimation in legged robot navigation. However, conventional Iterative Closest Point (ICP)-based LIO frameworks provide only pose constraints. Their position estimates often exhibit centimetre-level jitter due to LiDAR measurement noise, especially when the robot is stationary or moving slowly. This temporal inconsistency degrades the performance of downstream perception-driven motion planning and control. In this paper, we propose LiDAR–Inertial–Joint Odometry (LIJO), a novel state estimation framework for quadruped robots that integrates LiDAR, IMU, and joint encoder measurements within a manifold extended Kalman filter (EKF). The torso velocity is first estimated from joint angles and angular velocities via forward kinematics and is then used as a
In geometrically degenerate environments such as corridors and tunnels, LiDAR–Inertial odometry often drifts due to insufficient constraints, while conventional cameras can provide unreliable visual constraints under HDR lighting and severe motion blur. Event cameras offer microsecond-level temporal resolution and a high dynamic range, providing an additional information source for state estimation under challenging illumination and fast motion. In this work, we propose FAST-LIEO2 (and FAST-LIEVO2 with optional RGB fusion), a tightly-coupled LiDAR–inertial–event(–RGB) odometry framework that fuses LiDAR, event, and optional visual measurements via a sequentially updated error-state iterated Kalman filter (ESIKF) for efficient and stable multi-modal state estimation. To mitigate over-accumulation in highly active regions while preserving structure in low-activity areas, we design an activity-adaptive smoothed time surface (AS-TS) that adjusts the temporal decay parameter based on event activity. We further maintain two event submaps and design a coarse-to-fine hierarchical event update strategy, performing frame-to-frame geometric alignment followed by frame-to-map edge alignment, to improve prior pose correction and enlarge the convergence basin. Experiments on the public benchmark datasets and a self-collected dataset show that FAST-LIEO2 outperforms the compared multi-sensor SLAM baselines in accuracy and robustness across diverse structural and illumination conditions, while achieving strong real-time performance.
Jiang Wu, Zirui Wang, Jing Wu et al.· IEEE Robotics and Automation...· 0 citations
In robotic operation scenarios, LiDAR-Inertial SLAM systems based on factor graph optimization often lack sufficient adaptability. Common issues include backend optimization latency leading to odometry state divergence, and performance degradation in the scan-to-map matching mechanism due to local map bloat when operating in fixed areas for extended periods. To address these challenges, this paper proposes a lightweight LiDAR-Inertial SLAM framework, ALIO-SAM, tailored for mobile robots. ALIO-SAM adaptively switches between LIO and LO modes according to the backend latency of the backend factor graph optimization to ensure stable system operation. To tackle the issue of varying constraints caused by LiDAR point distances, we devise a distance-weighted strategy to enhance scan-to-map matching accuracy. Furthermore, ALIO-SAM introduces a dynamic keyframe selection strategy based on Scan Context[Formula: see text] for local map construction, preventing map redundancy under specific operational conditions. The proposed method is validated on real-world scenes and the public M2DGR dataset. Experimental results demonstrate that ALIO-SAM achieves positioning accuracy comparable to or exceeding open-source frameworks such as LeGO-LOAM, LIO-SAM, FAST-LIO2, and Light-LOAM, and it simultaneously reduces the computational time of frontend scan-to-map matching.
Baocun Wang, Quanyu Wu, Xiao-Dong Lu et al.· International Journal of Com...· 0 citations
Over the past decade, the fusion of light detection and ranging (LiDAR) and inertial navigation systems (INS) has become a reliable solution for environmental perception in intelligent mobile platforms. However, LiDAR performance degrades under adverse weather conditions. Millimeter-wave radar offers complementary benefits: it is robust to environmental disturbances and provides direct Doppler velocity measurements. Moreover, most existing fusion frameworks rely on the extended Kalman filter (EKF), which suffers from linearization errors and inconsistency. To address these limitations, we propose and derive a tightly-coupled LiDAR–radar–inertial odometry framework based on the equivariant filter (EqF). The framework leverages both LiDAR and radar to provide complementary constraints on the 9-dimensional navigation state, significantly improving accuracy and robustness in challenging environments. Furthermore, we conduct an in-depth observability analysis using Lie derivative theory, examining both the nonlinear system and its discrete filter system. We show that the EqF preserves the same unobservable directions as the underlying nonlinear system, thereby ensuring consistent state estimation, a property that standard EKF does not possess. In addition, experiments on real-world datasets demonstrate that our method outperforms the state-of-the-art LiDAR–inertial odometry approach and other EKF-based approaches in terms of localization accuracy, robustness and velocity estimation precision. Note to Practitioners—This work aims to provide a high-precision and robust fusion framework for LiDAR, radar, and INS in intelligent mobile platforms such as autonomous vehicles and drones. On such platforms, most existing EKF-based LiDAR-inertial odometry methods suffer from performance degradation under adverse weather conditions and from the inherent inconsistency of the EKF. To address these challenges, we propose a tightly-coupled LiDAR–radar–inertial odometry framework based on the EqF. The approach exploits the symmetry of the semi-direct product group to jointly model the navigation state and IMU biases within a geometrically consistent structure. We further conduct an in-depth observability analysis, demonstrating that the proposed framework preserves the correct unobservable directions of the underlying nonlinear system. As a result, the system exhibits superior robustness against initialization errors and external disturbances, along with improved accuracy compared to traditional approaches.
Anbo Tao, Yarong Luo, Chi Guo et al.· IEEE Transactions on Automat...· 0 citations
Accurate and robust localization is fundamental to the autonomous operation of high-speed trains (HSTs). However, conventional satellite-based localization becomes unreliable in degraded environments, such as long tunnels, which pose serious challenges to continuous and precise train localization. To address this issue, a tightly coupled light detection and ranging (LiDAR)-inertial method is proposed for HSTs autonomous localization, which does not rely on global navigation satellite system (GNSS). Specifically, the method adopts an on-manifold error-state Kalman filter (ESKF), which effectively constrains error propagation under high-speed motion and ensures consistent state estimation. In addition, a point-by-point update mechanism is employed, allowing the system to perform state updates at each LiDAR point cloud, where all points are deskewed and sequential residual updates are performed. Moreover, a multiframe merged update aggregates several consecutive LiDAR scans into an information-consistent refinement step, improving robustness in degraded scenes. Extensive experiments are conducted on real-world HSTs, including multiple long tunnel environments. The proposed method outperforms existing LiDAR-based methods, especially during high-dynamic motion. Significantly, this study demonstrates the potential of the method to enable reliable autonomous localization for HSTs.
Tian Wang, Hai-Feng Song, Zixuan Zhang et al.· IEEE Transactions on Instrum...· 0 citations
Accurate localization and 3-D reconstruction are critical for unmanned surface vehicles (USVs) operating in dynamic marine environments. However, water surface reflections, global positioning system (GPS) interruptions, and motion disturbances caused by wind and waves significantly degrade LiDAR–inertial odometry (LIO) performance. This article proposes an adaptive event-triggered (AET) LIO (AET-LIO) framework that dynamically adjusts the pose estimation model based on a motion-intensity metric, enabling robust state estimation under varying sea conditions. Furthermore, a real-time water-reflection filtering method is introduced to enhance point cloud integrity without sacrificing computational efficiency. Experiments conducted in both calm and wind-and-waves scenarios demonstrate that the proposed system reduces localization root-mean-square error (RMSE) compared with state-of-the-art LIO methods, while achieving high-fidelity structures above water reconstruction. The results validate the effectiveness of the AET in mitigating environmental uncertainties and ensuring reliable mapping in GPS-challenged maritime settings.
Yabei Guo, Huigang Wang, Wei Qiang et al.· IEEE Transactions on Instrum...· 0 citations
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