Multiple Integrated Navigation Sensors for Improving Occupancy Grid FastSLAM

An autonomous vehicle must accurately observe its location within the environment to interact with objects and accomplish its mission. When its environment is unknown, the vehicle must construct a map detailing its surroundings while using it to maintain an accurate location. Such a vehicle is faced with the circularly defined Simultaneous Localization and Mapping (SLAM) problem. However difficult, SLAM is a critical component of autonomous vehicle exploration with applications to search and rescue. To current knowledge, this research presents the first SLAM solution to integrate stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The implementation combines the MINS path with LIDAR to observe and map the environment using the FastSLAM algorithm. In real-world tests, a mobile ground vehicle equipped with these sensors completed a 140 meter loop around indoor hallways. This SLAM solution produces a path that closes the loop and remains within 1 meter of truth, reducing the error 92% from an image-inertial navigation system and 79% from odometry FastSLAM

Improving Occupancy Grid FastSLAM by Integrating Navigation Sensors

When an autonomous vehicle operates in an unknown environment, it must remember the locations of environmental objects and use those object to maintain an accurate location of itself. This vehicle is faced with Simultaneous Localization and Mapping (SLAM), a circularly defined robotics problem of map building with no prior knowledge. The SLAM problem is a difficult but critical component of autonomous vehicle exploration with applications to search and rescue missions. This paper presents the first SLAM solution combining stereo cameras, inertial measurements, and vehicle odometry into a Multiple Integrated Navigation Sensor (MINS) path. The FastSLAM algorithm, modified to make use of the MINS path, observes and maps the environment with a LIDAR unit. The MINS FastSLAM algorithm closes a 140 meter loop with a path error that remains within 1 meter of surveyed truth. This path reduces the error 79% from an odometry FastSLAM output and uses 30% of the particles

Roadmap towards the redefinition of the second

International audienceAbstract This paper outlines the roadmap towards the redefinition of the second, which was recently updated by the CCTF Task Force created by the CCTF in 2020. The main achievements of optical frequency standards (OFS) call for reflection on the redefinition of the second, but open new challenges related to the performance of the optical frequency standards, their contribution to time scales and UTC, the possibility of their comparison, and the knowledge of the Earth’s gravitational potential to ensure a robust and accurate capacity to realize a new definition at the level of 10-18 uncertainty. The mandatory criteria to be achieved before redefinition have been defined and their current fulfilment level is estimated showing the fields that still needed improvement. The possibility to base the redefinition on a single or on a set of transitions has also been evaluated. The roadmap indicates the steps to be followed in the next years to be ready for a sound and successful redefinition