Search Results (252)

Autonomous robotic navigation in dynamic environments depends on sensor-derived occupancy maps that are often degraded by occlusion, localization error, dynamic blockage, incomplete observation, and perception noise. These uncertainties can make a nominally short route unsafe after deployment, motivating conservative route selection for collision-aware path planning under sensor-derived map uncertainty. We formulate Conservative Robust Route Selection (CRRS) as a finite-scenario robust optimization and route-selection framework for autonomous robotic path planning under this uncertainty. CRRS constructs a heterogeneous portfolio of candidate routes, scores each route using nominal and plausible-world information only, and applies a validation-frozen conservative override rule that defaults to the scenario ensemble unless a candidate route satisfies predefined feasibility, risk, clearance, and cost-ratio guards. The evaluation protocol separates implementation auditing, candidate-pool expansion, validation-based selector design, frozen confirmation, public-benchmark validation, simulated sensor-model validation, and a controlled validation–held-out mismatch stress test. On the generated 30-domain MovingAI-format benchmark, candidate-pool expansion finds strict-safe-superior candidates in 113/150 matched groups, and the frozen selector reduces plausible collision from 0.1250 to 0.0807 and held-out collision from 0.3053 to 0.2937. On an official long-distance MovingAI subset with 260 queries and a minimum start-goal distance of 100 cells, CRRS reduces the held-out collision from 0.9648 for the scenario ensemble to 0.8822, with 141 wins, zero losses, and 119 ties. In an additional LiDAR/SLAM-inspired simulated sensor-model validation on 200 routed official-query problems, CRRS reduces the held-out collision from 0.4059 to 0.3768 relative to the scenario ensemble. A validation–held-out mismatch stress ablation isolates the conservative override rule: CRRS differs from CVaR-only on 73/260 problems, obtains a lower or equal held-out collision in every comparison, and avoids the 18 harmful held-out losses incurred by CVaR-only relative to the scenario ensemble. The resulting claim is deliberately scoped: CRRS improves aggregate route robustness over a strong scenario-ensemble default on the evaluated robotic path-planning benchmarks, while real-time deployment, live sensor integration with calibrated sensors, physical robot validation, family-level variation, and benchmark-specific uncertainty models remain limitations. Full article

3D Gaussian Splatting (3DGS) has gained prominence in autonomous driving and robotics for its rendering efficiency and high-fidelity reconstruction capabilities. However, incremental 3DGS map construction at large scales remains challenging due to sensor sparsity and computational constraints. In this paper, we propose LV-GS SLAM, a novel system that integrates LiDAR and visual data for incremental, large-scale reconstruction with real-time tracking. This system provides fast and robust pose estimation while also enabling photorealistic rendering. We first employ a LiDAR odometry frontend that processes 30 Hz LiDAR inputs to provide robust initial poses. In our implementation, the complete LiDAR tracking pipeline runs at 15–19 Hz, while the mapping module performs incremental optimization on selected keyframes. To address the sparsity-induced surface discontinuity in conventional LiDAR-based reconstruction, we propose a novel depth propagation approach that initializes 3D Gaussian primitives using dense depth maps, achieving faster PSNR convergence with 9× fewer optimization iterations compared to direct LiDAR initialization. Furthermore, we develop a keyframe-based submap management framework that dynamically adjusts memory allocation based on both primitive density and inter-frame overlap ratio, effectively preventing GPU memory overflow. Our system has been validated on the KITTI dataset, achieving superior rendering quality compared with representative reproducible baselines. We further validate the robustness of the system on a quadruped robot platform, demonstrating satisfactory performance in both pose estimation and high-fidelity reconstruction. Full article

Autonomous mobile robots require robust perception and high-level reasoning to operate in complex indoor environments. While the Robot Operating System (ROS) provides a modular framework for mapping and navigation, classical pipelines lack semantic understanding and natural-language interaction. This paper presents a semantic-aware autonomous navigation framework implemented on a ROS 1 (Melodic) mobile robot equipped with a two-dimensional light detection and ranging (LiDAR) sensor and an RGB-D camera. The system integrates LiDAR-based simultaneous localization and mapping (SLAM), the ROS navigation stack (move_base), and a lightweight You Only Look Once (YOLO) object detector for real-time on-board perception, and it anchors detections into the metric map to build a semantic map. A large language model (LLM) interprets natural-language instructions and converts them into structured navigation goals. Perception and control run entirely on-board the Jetson Nano, whereas the LLM is invoked episodically as a cloud service, yielding a hybrid embedded/cloud architecture. In indoor trials over a semantic map of two object classes, the system grounded all ten multilingual commands to the correct objects at a reasoning cost of about one second, and safely rejected a command referring to an unmapped object. We present this as an in-depth single-platform case study: owing to the 4 GB memory budget, the on-board detector and the full navigation stack are time-multiplexed rather than run continuously in parallel; nonetheless, a single degraded end-to-end trial confirmed that perception, online semantic anchoring, language grounding, and navigation compose within one continuous session. The framework offers a low-cost, extensible basis for language-guided robots in smart environments. Full article

Single-tree diameter at breast height (DBH) and tree height (H) are fundamental parameters for forest inventory, forest structure characterization, and forest carbon stock estimation. However, single-source LiDAR data cannot simultaneously capture complete trunk and canopy structural information, limiting the accuracy of single-tree structural parameter estimation. To address this issue, a Bi-Directional Cross-Attention Fusion Network (BCAF-Net) is proposed to estimate DBH and H separately by integrating ground-based Simultaneous Localization and Mapping LiDAR (SLAM LiDAR) and Unmanned Aerial Vehicle LiDAR (UAV LiDAR) data. The framework employs a dual-branch encoder and a bidirectional cross-attention mechanism to establish cross-view structural relationships between trunk and canopy observations, enabling effective multi-source feature fusion. Experiments conducted at two urban forest sites demonstrated that BCAF-Net achieved the highest estimation accuracy, with RMSE of 0.82 cm for DBH and 0.91 m for H and corresponding R² values of 0.97 and 0.96, respectively. Furthermore, the model maintained stable performance under varying forest structural complexities, cross-site conditions, and tree species. These results demonstrate that cross-view structural interaction effectively exploits complementary information from SLAM LiDAR and UAV LiDAR data, thereby improving single-tree structural parameter estimation in complex forest environments. Full article

Exploiting the complementary views of ground-based and aerial laser scanning requires co-registering their point clouds, which is difficult in closed tropical forest where Global Navigation Satellite System (GNSS) reception fails. We co-registered handheld Simultaneous Localization and Mapping (SLAM) LiDAR and UAV LiDAR without per-tree GNSS, using girth-identity anchors inherited from a companion inventory, over an open dry dipterocarp forest (DDF) and a closed dry evergreen forest (DEF) at the SERS, northeastern Thailand. Coarse-to-fine registration reduced the residual to a mean absolute distance of 0.17–0.18 m, and the clouds sampled complementary strata-stems and understory to about 25–30 m, emergent canopy to about 37 m. The co-registered data give each tree a ground-measured stem diameter and an aerially measured height, which we used to test whether diameter can instead be inferred from the canopy. It cannot: both a height-based and a best-case crown-and-height model saturated near 50 cm while measured stems reached about 100 cm, so UAV-only individual-tree carbon did not track the field reference; supplying the ground-measured diameter restored agreement (R² = 0.91), validated against reflective-tape anchor trees. The horizontal offset between a stem base and its crown apex further explains why position-only stem-to-crown matching fails and why girth-identity linkage is needed. We conclude that credible individual-tree carbon in closed tropical forest requires ground measurement of stem diameter, made possible by GNSS-independent SLAM-UAV co-registration; the aerial platform contributes height and coverage, not diameter. Full article

Mountainous road corridor landslides pose major dangers to infrastructure and transportation networks in areas with rugged terrain, fractured limestone lithology, and road-induced instability. This research presents a segment-based Random Forest (RF) model to evaluate landslide susceptibility along a mountainous road corridor spanning about 20 km in northeastern Iraq. Ten conditioning factors for landslide susceptibility were determined using Google Earth Engine (GEE) and GIS analysis. The model, validated using a 70/30 train/test split, achieved a mean cross-validation AUC of 0.783 ± 0.072 and an independent test AUC of 0.725 (accuracy = 0.735; Cohen’s Kappa = 0.401; recall = 0.750). To carry out independent multi-scale validation, the RF susceptibility maps were compared with a high-resolution SLAM LiDAR–AHP susceptibility approach within an overlapping ~2 km subsection, providing cross-scale validation of corridor-scale RF susceptibility predictions using a high-resolution susceptibility mapping framework. Comparison of the two approaches showed high spatial agreement, with 88.9% of the 18 overlapping segments exhibiting exact or one-class agreement between the two approaches. Full article

Aiming at the current SLAM (Simultaneous Localization and Mapping) algorithms in urban scenarios, which have problems such as elevation drift, odometry drift, and the appearance of false loop closures, a tightly coupled SLAM method with LiDAR and inertial guidance is proposed. In the front-end, a raster-based point cloud feature extraction method is introduced, enabling simultaneous segmentation and extraction of line, surface, and ground features. Utilizing the alignment results of line and surface features as the initial value for ground point alignment, interpolation weights are determined based on roll and pitch angle errors, effectively reducing global elevation errors through frame-by-frame constraints. The back-end employs an error state-based Kalman filter (ESKF) for GPS and IMU data fusion, enhancing the validity of true state estimation. A Scan Context loop closure detection method is designed, augmented by GPS detection as an auxiliary loop closure constraint to mitigate false loop closures. A global factor graph optimization model is also proposed. Experimental results demonstrate that, compared to existing open-source algorithms, the proposed method exhibits improved performance in structured urban scenes, reducing the average RMSE APE by 47.4% compared with LiDAR-only methods and by 22.9% compared with tightly coupled LiDAR-inertial methods. This work highlights the potential of multi-sensor fusion SLAM for achieving high-precision 3D localization and mapping in complex urban environments. Full article

Autonomous inspection of ballast water tanks requires three-dimensional (3D) LiDAR-based simultaneous localization and mapping (SLAM) in Global Positioning System (GPS)-denied, geometrically repetitive interiors, where sensing, mapping, and control modules share a limited onboard memory budget. Graph SLAM backends that rely on sparse factorization can incur fill-in, increasing peak memory and limiting deployment on edge computers. The proposed architecture couples a robust hierarchical bundle adjustment frontend with a factorization-free Kaczmarz backend. The frontend combines residual-adaptive weighting, damped and bounded pose updates, soft fallback, local-map compression, and memory-aware keyframe control. The backend stores the whitened Jacobian in compressed sparse row (CSR) format and performs row-wise projections without explicitly forming the normal equations, a Cholesky factor, or a transpose cache. Evaluation was conducted on Norwegian University of Science and Technology (NTNU) Ballast Water Tank missions 1–3, containing 851, 1202, and 1084 LiDAR frames. Following robust local bundle adjustment and verified similarity alignment, translational root-mean-square errors were 0.080, 0.110, and 0.127 m, corresponding to 0.137%, 0.143%, and 0.122% of the reference path lengths; archived baseline ratios ranged from 0.281% to 0.372%. These results support a numerical architecture that combines frontend stabilization, row-wise optimization, and memory-aware policies for resource-constrained marine inspection robots. Full article

Soil spatial variability is a key driver of tree development in perennial crops, and its characterisation is essential for precision orchard management. Against this background, soil–canopy relationships were investigated in a Citrus reticulata Blanco cv. Tango orchard under Mediterranean conditions. Electromagnetic induction (EMI), unmanned aerial vehicle (UAV) multispectral imagery, and mobile LiDAR data registered using a Simultaneous Localisation and Mapping (SLAM) workflow were integrated at individual-tree level. A previously validated EMI-derived apparent electrical conductivity (EC_a) layer was used as a baseline descriptor of soil variability. UAV and mobile LiDAR acquisitions were harmonised for 40 trees: LiDAR point clouds were voxelised to derive canopy structural traits, while UAV imagery provided Soil-Adjusted Vegetation Index (SAVI) values. EC_a at 14 kHz was negatively correlated with canopy volume (r = −0.605, R² = 0.365) and canopy volume-to-projected area ratio (r = −0.571, R² = 0.326), both significant at p < 0.001. Conversely, SAVI showed a weaker, non-significant relationship with EC_a (r = −0.285, R² = 0.081, p = 0.0749). The reduced multiple linear regression model explained canopy volume variability (R² = 0.804), retaining canopy diameter and EC_a as significant predictors. These findings highlight the value of LiDAR-derived structural traits as sensitive indicators of soil-related canopy variability, supporting the integration of structural, spectral, and soil-sensing data for site-specific orchard management. Full article

Line-driven soft robots possess inherent advantages in terms of cushioning and terrain adaptability, but the controllable deformation design of line-driven structures and its coordination mechanism with the overall robot motion remain insufficiently studied. To fill this gap, this paper designs a line-driven hexapod soft robot that achieves directional bending of flexible legs through unilateral line traction, combined with triangular gait co-motion and ROS-based multi-sensor perception. Integrating leg deformation as part of the motion mechanism enables the robot to achieve straight-line and turning movements while maintaining structural compliance. This paper establishes the mapping relationship between the leg actuation space, configuration space, and task space, constructs a kinematic model, and uses the finite element method to analyze leg deformation and stress distribution. Based on this, a robot prototype is built, and a ROS-based distributed control and perception system is constructed, utilizing LiDAR, camera, and attitude sensor data to achieve SLAM and state monitoring. Experimental results show that the robot can achieve continuous motion with an average speed of 15.32 mm/s and a turning angle of 4.75° in a single gait cycle. The feasibility of line-driven structure control based on unilateral traction was verified, and a reference was provided for the design of soft robots oriented towards environmental perception. Full article

This study evaluates an Adaptive Monte Carlo Localization-Extended Kalman Filter (AMCL-EKF) pose-estimation stack for repeatable 2D LiDAR SLAM in GPS-denied indoor inspection scenarios. AMCL was used as an online map-referenced correction source fused with LiDAR odometry and Inertial Measurement Unit (IMU) data, and the resulting pose estimate was supplied online to three SLAM backends: Cartographer, GMapping, and SLAM Toolbox. Experiments were performed with a wheeled Husarion Panther and a quadruped Boston Dynamics Spot in three indoor environments of different geometric complexity, producing 720 SLAM executions. Trajectory repeatability was assessed using SE(2)-aligned pairwise and centroid-based ATE-style dispersion and translational RPE, while map repeatability was evaluated with occupied-cell IoU. Accordingly, the metrics were used to quantify between-run dispersion rather than absolute accuracy against external ground-truth data. The results show that AMCL-EKF fusion is highly dependent on the environment, platform, and SLAM backend. AMCL improved selected configurations, especially for Spot in structured environments and for Panther map consistency, but degraded others in geometrically repetitive corridors and mixed-structure spaces. The study also shows that the presence of AMCL-assisted odometry correction alone does not determine final trajectory repeatability, because each SLAM backend incorporates the supplied fused pose estimate differently. The findings support confidence-aware AMCL integration and motivate integrated SLAM architectures resistant to over-correction. These results provide guidance for robust autonomous mapping and inspection with heterogeneous mobile robotic platforms in real environments. Full article

Learning-based approaches have significantly advanced the capabilities of Simultaneous Localization and Mapping (SLAM) systems, particularly in challenging environments characterized by noise, dynamic objects, and perceptual ambiguity. However, the literature remains highly heterogeneous in terms of sensing modalities, datasets, evaluation protocols, and reporting practices, making systematic comparison difficult. This paper presents a taxonomy-driven review of learning-based SLAM approaches, with particular emphasis on LiDAR-based systems in mobile robotics, and introduces a functional taxonomy that categorizes methods according to the role of learning within the SLAM architecture: (i) learning-enhanced front-end SLAM (T1), (ii) learning-enhanced back-end SLAM (T2), and (iii) learning-centric SLAM systems (T3). Representative studies were analyzed with respect to performance characteristics, robustness, computational requirements, datasets, and deployment-related evidence. The analysis shows that T1 approaches primarily improve local pose estimation and robustness, T2 methods enhance global consistency through learning-based loop closure and relocalization, and T3 approaches explore unified representations, semantic reasoning, and learning-centric autonomy, albeit with greater computational demands and limited deployment evidence. The review further indicates that hybrid approaches combining geometric and learning-based components constitute a prominent trend in the literature, frequently reporting improvements in accuracy and adaptability while maintaining compatibility with established SLAM frameworks. Nevertheless, these observations should be interpreted cautiously, as stronger empirical evidence for hybrid systems may partially reflect their greater technological maturity and broader evaluation history. Finally, the review identifies persistent challenges, including limited cross-domain generalization, high computational requirements, limited deployment-oriented evaluation, and the lack of standardized benchmarking and reporting practices. These findings highlight the need for more reproducible evaluation methodologies, uncertainty-aware learning strategies, and computationally efficient architectures for robust real-world autonomous SLAM. Full article

To address the problem of high-frequency roll disturbances generated during dynamic balancing in non-coaxial two-wheeled robots, this paper proposes a Rotation-Optimized Inertial–Visual SLAM system (ROIV-SLAM) for robust state estimation. The proposed approach adopts a decoupled architecture for translation and rotation estimation. In the front-end, an Extended Kalman Filter (EKF) is employed to fuse LiDAR, an inertial measurement unit (IMU), and wheel odometry to obtain an initial translation estimate. Meanwhile, a physical manifold constraint is constructed using the gravity vector and surface normals extracted from RGB-D point clouds, supporting stable rotation estimation under high-frequency disturbances through Lie-group-based optimization. In the back-end, a factor graph is established, and loop closure robustness is enhanced through vision–LiDAR scan matching. Experimental results indicate that ROIV-SLAM achieves improved trajectory consistency with respect to the optimized reference trajectory and more robust mapping performance compared with the evaluated baseline approaches in the tested scenarios. The results further suggest that introducing task-specific physical dynamic constraints and a decoupled estimation mechanism helps suppress high-frequency motion noise inherent to balancing robots, thereby improving the robustness of state estimation in complex environments. Full article

Per-tree geolocation in closed tropical canopies has typical uncertainties of 5–15 m with GNSS receivers, preventing automated linking of field inventories to point-cloud stem data. We propose an anchor-based matching framework that does not require per-tree GNSS. A handheld SLAM LiDAR scanner maps stems and girths within ≈40 min; field crews record species, girth, and serial numbers without physical markers or tools. Dataset linkage uses a small subset of reflective-tape anchor trees (35 and 43 per hectare, roughly one per 400–500 m²) with approximate GNSS locations. Species identity is transferred using median-based GNSS bias correction and quadrant-partitioned Hungarian matching with global deduplication; accuracy is validated by leave-one-anchor-out (LOAO) tests and exact binomial statistics. Tested in two 1-ha plots of open Dry Dipterocarp Forest (DDF; 280 trees/ha) and dense Dry Evergreen Forest (DEF; ~1054 trees/ha) at the Sakaerat Biosphere Reserve, Thailand, SLAM girth matched tape data with R² = 0.997, RMSE = 1.82 cm in DDF; in DEF, after correcting a 6.38 m GNSS bias, R² = 0.986 and RMSE = 7.01 cm, with ≥99% detection for stems ≥30 cm girth (99.2% DDF; 100% DEF). LOAO accuracy was 35/35 in DDF and 40/43 in DEF. Retroreflective-tape anchors were additionally detected automatically from the SLAM intensity channel in 71.4% of DDF anchors (95% CI 53.7–85.4) and 76.7% of DEF anchors (95% CI 61.4–88.2) at intensity ≥ 150 DN, with up to 59-fold enrichment over matched non-anchor controls at I ≥ 250 in DEF (Fisher’s exact p < 1 × 10⁻¹⁵), enabling a fully automated anchor-detection pipeline. Full article

Lunar lava tubes are subsurface cavities generated by volcanic activity and are regarded as promising targets for exploration because they can offer natural shielding and potentially support future lunar infrastructures as protected shelters and scientific laboratories. Autonomous navigation in these environments remains challenging due to the absence of illumination, sparse or repetitive geometric features, uneven terrain, and intermittent communications that limit teleoperation. In this framework, the Italian Space Agency (ASI) is pursuing a dedicated mission, and OHB Italia has been appointed the prime contractor to perform a candidate system-architecture study for lava tube exploration. This paper presents the activities and results related to the definition of the subsurface Guidance, Navigation, and Control (GNC) algorithm for a rover/hopper system. To address the above constraints, this study investigates the requirements for autonomous onboard navigation, focusing on sensor selection for Simultaneous Localization and Mapping (SLAM) as a fundamental prerequisite for mission success. A weighted-criteria evaluation framework is developed to assess various sensing modalities, considering mission-specific constraints. Based on this analysis, a sensor configuration optimized for GPS-denied and unilluminated environments is proposed. The effectiveness of the selected sensing architecture is validated through a simulation campaign conducted in simulation environments (CoppeliaSim v4.10.0/MATLAB 2025a), using two representative SLAM pipelines (ICP and LOAM) in LiDAR-only and LiDAR + IMU configurations. Finally, a modular Guidance, Navigation, and Control (GNC) architecture incorporating frontier-based exploration is proposed. Full article