Radar-Inertial ICP-based Pose Graph SLAM @autonomousrobotslab

Uploaded September 2022 | Updated September 2026, 3 weeks ago

A plethora of localization solutions for different sensor modalities exists, yet robust sensing in degraded perception conditions remains an open challenge. For example, the perception sensors most typically used in mobile robotics, like visible-light cameras and LiDARs, can face significant problems when operating in environments with dense obscurants (e.g., fog, smoke or dust). However, millimeter wave radars offer an avenue to penetrate such conditions and overcome these limitations. Motivated by the above, in this work we propose a new radar-inertial SLAM method that utilizes doppler velocity-based radar-inertial odometry combined with aggregate radar point cloud registration on both short- and long-term (loop closure) associations. Experimental evaluations are conducted on both publicly available and self-collected datasets and allow to demonstrate the performance of the method.

Radar-Inertial ICP-based Pose Graph SLAM

$Resource-aware Online Parameter Adaptation for Computationally-constrained Visual-Inertial Odometry In this work, a computational resources-aware parameter adaptation method for visual-inertial navigation systems is proposed with the goal of enabling the improved deployment of such algorithms on computationally constrained systems. Such a capacity can prove critical when employed on ultra-lightweight systems or alongside mission critical computationally expensive processes. To achieve this objective, the algorithm proposes selected changes in the vision front-end and optimization back-end of visual-inertial odometry algorithms, both prior to execution and in real time based on an online profiling of available resources. The method also utilizes information from the motion dynamics experienced by the system to manipulate parameters online. The general policy is demonstrated on three established algorithms, namely S-MSCKF, VINS-Mono and OKVIS and has been verified experimentally on the EuRoC dataset. The proposed approach achieved comparable performance at a fraction of the original computational cost.$

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