6 accepted papers
LiDAR semantic segmentation is crucial for autonomous vehicles and mobile robots, requiring high accuracy and real-time processing, especially on resource-constrained embedded systems. Previous state-of-the-art methods often face a trade-off between accuracy and speed. Point-based and sparse convolu
Magnetometer-based indoor navigation uses variations in the magnetic field to determine the robot's location. For that, a magnetic map of the environment has to be built beforehand from a collection of localized magnetic measurements. Existing solutions built on sparse Gaussian Process (GP) regressi…
This article proposes a novel indoor magnetic field-based place recognition algorithm that is accurate and fast to compute. For that, we modified the generalized “Hough Transform” to process magnetic data (MagHT). It takes as input a sequence of magnetic measures whose relative positions are recover…
We propose in this letter a tightly-coupled fusion of visual, inertial and magnetic data for long-term localization in indoor environment. Unlike state-of-the-art Visual-Inertial SLAM (VISLAM) solutions that reuse visual map to prevent drift, we present in this letter an extension of the Multi-State
Cameras and 2D laser scanners, in combination, are able to provide low-cost, light-weight and accurate solutions, which make their fusion well-suited for many robot navigation tasks. However, correct data fusion depends on precise calibration of the rigid body transform between the sensors. In this…
This paper introduces a topological localization algorithm that uses visual and Wi-Fi data. Its main contribution is a novel way of merging data from these sensors. By making Wi-Fi signature suited to FABMAP algorithm, it develops an early-fusion framework that solves global localization and kidnapp…