Jul 2026· 2026 6th International Conference on Inventive Computation and Information Technologies (ICICIT)· pp. 114-117· 0 citations· 14 references
Abstract
Robotic platforms operating in GPS-denied environments require robust ego-motion estimation systems that fuse complementary sensor modalities under onboard computational constraints. This paper proposes a navigation framework estimating six-degree-of-freedom (6 DoF) robot pose in unstructured scenes using a monocular camera stream, inertial measurement unit (IMU) data, and sparse depth cues within the multi-state constraint Kalman filter (MSCKF) architecture. The key innovation integrates 3D landmark measurements into visual feature tracks, reducing positional uncertainty and drift accumulation compared to vision-only approaches. The method is efficient enough for resource-constrained systems such as micro aerial vehicles and small ground robots. The measurement fusion strategy is analytically derived and evaluated on aerial robot trajectory datasets. Results show improved tracking accuracy and stability in challenging indoor and outdoor scenarios without GPS, enabling prolonged autonomous missions in complex 3D environments with real-time pose feedback and low computational burden.
Reliable localization is required for autonomous mobile robots when individual sensing streams become noisy, intermittent, or unavailable. This study evaluates a multi-sensor fusion framework that combines LiDAR, monocular vision, GPS, UWB, and IMU data using three strategies: (i) a baseline Extended Kalman Filter (EKF); (ii) a dual-stage sequential EKF that refines LiDAR-Inertial Odometry (LIO) before the final fusion stage; and (iii) a hybrid learning-filtering approach in which modality-specific learned motion and position estimates are incorporated into an EKF. All evaluations were conducted in ROS-Gazebo under nominal operation and controlled sensor-degradation/dropout conditions. Relative to the controller-derived reference trajectory, the standard EKF achieved 0.2235 m RMSE and the dual-stage EKF achieved 0.2029 m RMSE, a descriptive reduction of approximately 9.2% for the reported run. The hybrid learning-EKF achieved 0.212 m RMSE under nominal sensing and 0.384 m RMSE during the tested failure sequence. These results support the evaluated fusion designs under the reported simulation conditions, but they do not establish statistical generalization or universal real-world resilience; independent ground truth, repeated trials, GPS ablation, and physical validation remain necessary.
Muhammad Shahzad Alam Khan, Anas Bin Aqeel, Hassan Elahi et al.· Scientific Reports· 0 citations
: 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
Indoor mobile robots equipped with low-cost and sparse sensors often suffer from limited vertical perception and dynamic residual artifacts in the final map. This paper presents a lightweight 2.5D simultaneous localization and mapping (SLAM) framework using a single-line laser distance sensor (LDS), time-of-flight (ToF) sensing, wheel odometry, and an inertial measurement unit (IMU). In this work, 2.5D refers to a 2D grid map with discretized vertical occupancy bins for each grid cell, rather than a full continuous 3D reconstruction. The system integrates multi-sensor synchronization, motion correction, error-state Kalman filter (ESKF)-based state estimation, normal distributions transform (NDT) registration, and pose graph optimization to reconstruct a pose-consistent global map. Based on this map, an offline dynamic refinement module estimates temporal voxel support across keyframes, extracts low-support candidate regions, and applies geometric clustering and isolated-point filtering to suppress transient residual artifacts while preserving stable structures. A 24-bit RGB occupancy encoding is further proposed to store the discretized vertical occupancy state in a compact three-channel image format. The proposed framework emphasizes system-level deployment value by combining sparse multi-sensor mapping, conservative offline refinement, and compact height-aware map export on a low-cost indoor robot platform. Experiments on public datasets, embedded hardware, and self-collected indoor sequences evaluate odometry reference performance, resource usage, platform-specific 2.5D mapping, dynamic refinement, and height-aware encoding.
Guitao Yu, Yuping Zhang, Zhiao Qi et al.· Italian National Conference...· 0 citations
Autonomous mobile robots operating in dynamically changing, unstructured environments require high-precision, drift-free localization capabilities to achieve robust operational safety and navigational efficacy. While visual Simultaneous Localization and Mapping (vSLAM) and Inertial Navigation Systems (INS) serve as foundational technologies in intelligent automation, standalone implementations encounter significant vulnerabilities, specifically optical occlusion and cumulative dead-reckoning drift. This paper presents a comprehensive study on an intelligent, optimization-based, tightly-coupled vision-inertial sensor fusion framework designed for robust localization in challenging environments. The proposed system integrates high-frequency inertial measurements from an Inertial Measurement Unit (IMU) with high-fidelity visual landmarks extracted from a monocular camera, utilizing an artificial intelligence-driven adaptive Extended Kalman Filter (EKF) state estimation matrix to dynamically adjust measurement noise weights. By analyzing the structural characteristics of feature tracking alongside high-frequency acceleration profiles, the intelligent layer dampens sensor anomalies caused by aggressive motion or lightning fluctuations. Experimental validations conducted using the EuRoC MAV public benchmark dataset indicate that the proposed intelligent fusion architecture provides superior performance across dynamic trajectories, reducing the Absolute Trajectory Error (ATE) by up to 34% compared to classical loosely-coupled filtering methods while maintaining sub-centimeter positional drift thresholds.
J. Arsac· International Journal of Int...· 0 citations
Reliable navigation in GPS-denied environments remains a critical challenge for autonomous vehicles (AVs), particularly in complex indoor and urban settings. GPS-based localization systems often fail under these conditions, highlighting the need for resilient multimodal solutions. In this article, we present a radar-assisted tracking system that integrates LiDAR and inertial measurements within a sensor-fusion architecture to achieve robust navigation. The principal methodological contribution is a unified tracking and prediction framework that combines Bayesian state estimation with learning-based temporal prediction, enabling accurate tracking while continuously forecasting the slave robot’s short-term future state from mapping observations generated by the master robot, with a typical end-to-end perception-to-action latency of 20–60 ms. The communication and prediction forecasting module operates with an update interval below 35 ms, enabling real-time cooperative robotic operation. Sensor data are fused through a pipeline incorporating Gaussian Mixture Models (GMMs) for post-processing, which helps mitigate the limitations associated with individual sensors during edge processing. Moreover, Kalman filtering is employed to mitigate sensor noise and drift, thereby improving state estimation accuracy through trajectory smoothing. The fused spatiotemporal information is subsequently exploited by a Convolutional Recurrent Neural Network (CRNN) coupled with a Nonlinear Autoregressive model with eXogenous Inputs (NARX) to model the robot’s motion dynamics and provide short-horizon state prediction. Through simulations and real-world indoor experiments conducted in GPS-denied environments, we validate the system’s ability to provide accurate and continuous pose estimation with low localization errors. Experimental results show that the proposed framework achieves root-mean-square errors of 0.12 m, 0.15 m, and 0.28 m along the X, Y, and Z axes, respectively, while maintaining sub-meter maximum position deviations throughout the evaluated trajectories. These results confirm that the proposed framework provides reliable localization and predictive state estimation for cooperative robotic navigation in indoor GPS-denied environments. Future work will investigate outdoor validation and extend the framework to additional data-driven decision-making models for future robotic services.
Z. Ezzouine, Adil Salbi, Mohamed Abouzahir et al.· Entropy· 0 citations
Autonomous robot localization is a critical function that enables intelligent navigation, motion planning, and interaction within structured and unstructured environments. Before 2019, significant advancements were made by integrating multiple sensors such as wheel encoders, IMUs, LiDAR, cameras, ultrasonic sensors, and GNSS. Since each sensor has limitations like drift, uncertainty, and environmental sensitivity, advanced sensor fusion techniques were developed to improve localization accuracy and reliability. This study examines localization methods based on probabilistic filtering approaches including Kalman Filter, Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF), and graph-based optimization. A multi-layer sensor fusion architecture combining odometry, inertial sensing, LiDAR, and vision-based observations is proposed for accurate robot pose estimation in dynamic environments. Experimental results demonstrate that multi-sensor fusion significantly improves localization accuracy, reduces drift, enhances robustness against sensor failures, and increases adaptability in indoor environments. Metrics such as RMSE, trajectory consistency, heading accuracy, covariance stability, and computational efficiency were used for evaluation. The integration of LiDAR, IMU, and wheel odometry reduced localization error by over 90% compared to wheel odometry alone, while vision-based loop closure further improved map consistency. Overall, the study highlights that advanced sensor fusion techniques provide an effective and reliable solution for autonomous robot localization and continue to influence modern robotic navigation systems.
Carlos Hugo Ribeiro Mendes· International Journal of Int...· 0 citations
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