11 accepted papers
On-demand delivery platforms rely on Travel Time Estimation (TTE) to balance courier earnings and overdue risks. In collaboration with one of China's largest platforms, we address a critical "Fairness Gap" in TTE: current systems fail to capture complex delivery patterns in GNSS-denied environments,
In complex environments, traditional path planning methods rely on manually defined models, requiring tedious adjustments under varying scenarios or constraints. They also suffer from unstable time overhead and exponentially increasing computational costs as environmental complexity grows. Deep lear…
Human Activity Recognition (HAR) is crucial for applications like urban traffic management and health monitoring but faces challenges in handling complex patterns and missing sensor data. In this work, we propose a conditional Kolmogorov-Arnold network diffusion (CKAD) framework for HAR, which separ…
We introduce Crowd-Sourced Splatting (CSS), a novel 3D Gaussian Splatting (3DGS) pipeline designed to overcome the challenges of pose-free scene reconstruction using crowd-sourced imagery. The dream of reconstructing historically significant but inaccessible scenes from collections of photographs ha…
Visual dense SLAM can facilitate pose estimation and map reconstruction for sensor carriers in unknown environments. However, in uncontrolled environments such as offices, shopping malls, and train stations, frequent occurrences of people walking back and forth or temporary movement of objects withi
The success of 3D Gaussian splatting in 3D reconstruction has recently led to efforts to integrate it with SLAM systems. However, most existing research has focused on indoor tracking and mapping, while outdoor Gaussian SLAM methods still heavily rely expensive LiDAR sensor. To address these challen
Map-free visual relocalization computes camera pose using only a query image and a reference image. Therefore, it is hindered by challenges in feature-point matching and the absence of scale information in monocular images. These issues may cause significant rotational and metric errors, leading to…
Recently, significant advancements have been made in 3D Gaussian Splatting SLAM for dynamic environments. However, most existing methods primarily address active dynamic objects, such as people and vehicles, and fail to account for the impact of passive dynamic objects on localization and mapping. T
Neural implicit representation has recently achieved significant advancements, especially in the field of SLAM(Simultaneous Localization and Mapping). Previous NeRF-based SLAM methods have difficulties with object-level localization and reconstruction and struggle in dynamic and illumination-varied…
In large-scale outdoor environments, vehicles often encounter situations like retracing their path or turning around, leading to many reverse loop closures where the vehicles traverse previously covered paths from opposite viewpoints. Existing monocular SLAM methods, due to insufficient utilization…
State estimation with sensors is essential for mobile robots. Due to different performance of sensors in different environments, how to fuse measurements of various sensors is a problem. In this paper, we propose a tightly coupled multi-sensor fusion framework, Lvio-Fusion, which fuses stereo camera…