Igor Podtsepko, Yuriy Kim, Karam Mahfod, Evgeny Belyaev, Kristian Kirs, Jianping Lin, Ilya Papiev
原文摘要
In this paper, we consider the problem of recompression of images from JPEG to JPEG AI format. We show that direct recompression from JPEG to JPEG~AI trained on uncompressed images is accompanied by the preservation of the JPEG image artifacts in the resulting JPEG~AI image. In order to avoid it, we evaluate an approach where an artifacts removal neural network is first applied to the input JPEG image as an external pre-filter. However, in this case, the processing time is increased by 29 times. Therefore, we propose an artifact-compensated training for JPEG AI and show that it is able to combine pre-filtering against target distortions with efficient compression without increase in the processing time.
Autonomous UAV exploration is increasingly used for large-scale surveying and inspection in inaccessible or disaster-affected environments, where onboard memory and computational resources are often severely constrained. Most existing UAV exploration systems maintain globally queryable geometric map representations for motion planning and exploration decision-making, causing memory consumption and spatial-query costs to increase with the explored area. Robot-centric local mapping bounds dense-map memory and computation but discards global task context needed for long-horizon exploration.We present FLAVOR, a hierarchical spatio-temporal framework that confines dense metric geometry to a robot-centric local map while retaining compact global observation and task context. At the observation level, lightweight LiDAR-keyframe visibility records preserve ray-wise evidence for frontier reasoning. At the task level, unresolved frontiers beyond the local-map extent are retained as compact pending tasks associated with odometry anchors. A sparse topological graph connects these anchors through retained routes for future revisits.Upon returning to a task region, FLAVOR reconstructs the frontier and exploration viewpoint from observations, while the topological route is instantiated and validated against the current local map before execution. Dense-map capacity remains fixed as exploration expands, with exploration-dependent memory growth confined to compact observation and task context.FLAVOR achieves 31.98--41.88% shorter exploration time and a 94--99% smaller map memory footprint than the best baseline that completes all three simulated scenes. In real-world deployments, it reduces this footprint by 92.67% in the forest and 62.42% at the construction site relative to an explicit point-cloud ikd-tree while preserving long-term task continuity under constrained onboard resources.
Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces. In these settings, the sensing system must provide full-surround coverage, metric geometric cues, and stable target association under wide field-of-view distortion and occlusion. Existing image-plane trackers are tightly coupled to the camera projection and become unreliable in panoramic imagery, while conventional Euclidean 3D formulations introduce redundant directional parameters and do not naturally unify angular, scale, and depth estimation. In this paper, we present $\mathbf{S^3KF}$, a panoramic 3D multi-object tracking framework built on a motorized rotating LiDAR and a quad-fisheye camera rig. The key idea is a geometry-consistent state representation on the unit sphere $\mathbb{S}^2$, where object bearing is modeled by a two-degree-of-freedom tangent-plane parameterization and jointly estimated with box scale and depth dynamics. Based on this state, we derive an extended spherical Kalman filtering pipeline that fuses panoramic camera detections with LiDAR depth observations for multimodal tracking. We further establish a map-based ground-truth generation pipeline using wearable localization devices registered to a shared global LiDAR map, enabling quantitative evaluation without motion-capture infrastructure. Experiments on self-collected real-world sequences show decimeter-level planar tracking accuracy, improved identity continuity over a 2D panoramic baseline in dynamic scenes, and real-time onboard operation on a Jetson AGX Orin platform. These results indicate that the proposed framework is a practical solution for panoramic perception and industrial-scale multi-object tracking.The project page can be found at https://kafeiyin00.github.io/S3KF/.