Real-Time Temporally Consistent Monocular 6D UAV Pose Estimation for Onboard Aerial Perception

AMR-Pose: An Active LED Marker-Based Relative Pose Estimation Framework With Probabilistic Switching PnP for Cooperative AUVs

Reliable relative pose estimation between autonomous underwater vehicles (AUVs) is critical for cooperative ocean exploration, sampling, and multi-robot coordination. However, achieving robust vision-based relative localization in underwater environments remains challenging due to severe optical degradation, including turbidity, illumination variations, reflections, and intermittent feature occlusions. This paper presents AMR-Pose, an active LED marker-based relative pose estimation framework for cooperative AUVs. A compact marker module consisting of one red central LED and three blue peripheral LEDs is developed and integrated onto the leader AUV to provide distinctive visual features under complex underwater conditions. Building upon the detected marker observations, a probabilistic switching Perspective-n-Point estimator (PSwPnP) is developed by combining Lie-group pose propagation on $SE(3)$, probabilistic marker association, and visibility-adaptive measurement fusion for robust six-degree-of-freedom relative pose estimation. The proposed framework dynamically adapts the estimation process according to marker visibility, maintaining geometric consistency and temporal stability during partial observations and visibility transitions. Extensive water-tank experiments with motion-capture ground truth validate that AMR-Pose achieves accurate, smooth, and robust relative pose estimation under challenging underwater conditions. Closed-loop leader-follower experiments further demonstrate its feasibility for real-time relative pose feedback in cooperative underwater robotics.

AirAlign: Geometry-Aware Relative Pose Alignment for UAV Last-Meter Navigation

AirAlign is proposed, a framework for RGB-only image-pair relative pose alignment for UAVs, using a pretrained visual geometry reconstruction model as the backbone to extract geometry-aware features from source-target image pairs.

Autonomous 3D Object Geolocation on UAVs Using Monocular Camera and Gimbaled Laser

3D CNN and Study of Attention Mechanisms to Relative Camera Position

Calculating the relative position between images in Unmanned Aerial Vehicles (UAVs) is a core component in tasks such as visual odometry, SLAM, and state estimation. It enables the UAV to estimate its movement between frames using onboard sensors. In this paper, we present a spatio-temporal regression architecture combining 3D CNNs and attention mechanisms. The model takes a sequence of image frames and corresponding Inertial Measurement Unit (IMU) data for each frame as input and outputs the estimated relative position in meters between the images. The inclusion of IMU measurements is critical for improving robustness to motion blur, low-texture environments, and rapid maneuvers, as it provides complementary information about the UAV’s linear acceleration and angular velocity. The CNN layers extract compact spatio-temporal features from the video input, while the multihead attention layer captures temporal dependencies and contextual relations across time. The final Multi-Layer Perceptron (MLP) regresses these fused representations into a relative position estimate. We systematically evaluate different attention mechanisms: Masked, Causal, Local-window, and Linformer, to analyze their impact on accuracy and efficiency in relative position estimation. We demonstrate the effectiveness of our method on the TII Drone Racing and UZH-FPV datasets.

Monocular ORB-SLAM3 Evaluation for Multi-Altitude VTOL UAV Mapping

Abstract. Reliable visual localization is essential for long-range VTOL UAV mapping in GNSS-degraded environments. This paper presents a quantitative evaluation framework for monocular ORB-SLAM3 using a 66.48 km multi-altitude UAV mission and aerial-triangulation-derived camera poses as reference data. The workflow associates SLAM and reference trajectories by image key, applies Sim(3)-based metric alignment, corrects coordinate-axis inconsistency, and refines attitude by a global rotation offset, enabling full-mission and segment-level comparison in a common metric frame. The evaluation covers four altitude segments, namely 100, 150, 200, and 250 m AGL, under three protocols: No-Loop (NL), With-Loop Global Slice (GS), and With-Loop Local Re-Sim(3) (LR). For the full mission, the proposed alignment achieves a 3D position RMSE of 7.41 m over 5330 matched frames and substantially reduces the geometric deformation observed in the S+T baseline. Segment-level results show a strong altitude dependency in the isolated NL runs, with 3D RMSE decreasing from 22.95 m at 100 m to 5.49 m at 250 m. Among the three protocols, LR consistently yields the best segment-level position accuracy, reaching 4.00, 8.26, 3.94, and 3.92 m at 100, 150, 200, and 250 m, respectively. Long-range analysis further shows that the trajectory remains globally bounded, while cumulative 3D endpoint drift increases from 0.35 m at 50 m to 10.66 m at 25.6 km. These results indicate that ORB-SLAM3 can support large-scale trajectory estimation for UAV mapping, but its evaluated quality depends strongly on alignment, segmentation, and evaluation strategy.

RRTrack: Robust and Recoverable Object 6D Pose Tracking for Dynamic Scenes

Robust object 6D pose tracking is critical for robotic systems operating in dynamic and occluded scenes. Per-frame estimators are accurate but computationally expensive, while current trackers struggle with fast motion and complete occlusion due to their reliance on continuous visibility. To address these challenges, we present RRTrack, an efficient, recoverable object 6D pose tracker that enables robust tracking through fast motion and target disappearance--reappearance. RRTrack introduces a 2D--6D closed-loop tracking strategy that integrates memory-based video object segmentation (VOS) with 6D pose refinement. The 2D branch maintains target localization, and the 6D branch verifies geometric consistency before memory updates. In addition, a DINOv2-based dual-bank template matching module is developed to recover lost targets by jointly exploiting offline synthetic templates and online observation anchors while maintaining real-time efficiency. We also introduce a synthetic RGB-D benchmark comprising three robotic scenarios with fast motion and full occlusion. Experimental results on the synthetic benchmark demonstrate that RRTrack improves equal-subset mean ADD-S AR by 66.3\% and ADD-S AUC by 65.7\% over FoundationPose while achieving 55.2 FPS. Real-world experiments further validate the robustness of RRTrack under noisy sensing conditions. Project page: https://github.com/7kevin24/RRTrack