RaNDT SLAM: Radar SLAM Based on Intensity-Augmented Normal Distributions Transform

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 7831–7838

Abstract

Rescue robotics sets high requirements to perception algorithms due to the unstructured and potentially vision-denied environments. Pivoting Frequency-Modulated Continuous Wave radars are an emerging sensing modality for SLAM in this kind of environment. However, the complex noise characteristics of radar SLAM makes, particularly indoor, applications computationally demanding and slow. In this work, we introduce a novel radar SLAM framework, RaNDT SLAM, that operates fast and generates accurate robot trajectories. The method is based on the Normal Distributions Transform augmented by radar intensity measures. Motion estimation is based on fusion of motion model, IMU data, and registration of the intensity-augmented Normal Distributions Transform. We evaluate RaNDT SLAM in a new benchmark dataset and the Oxford Radar RobotCar dataset. The new dataset contains indoor and outdoor environments besides multiple sensing modalities (LiDAR, radar, and IMU).

Authors 3

1. Maximilian Hilger Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Mechanism Theory, Machine Dynamics and Robotics,Aachen,Germany

2. Nils Mandischer Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Mechanism Theory, Machine Dynamics and Robotics,Aachen,Germany

3. Burkhard J. Corves Aachen Institute of Mechanism Theory

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Institute of Mechanism Theory, Machine Dynamics and Robotics,Aachen,Germany