Semantic scene understanding is crucial for robust and safe autonomous navigation,
particularly so in off-road environments. Recent deep learning advances for 3D semantic …

Multi-modal data fusion has gained popularity due to its diverse applications, leading to an
increased demand for external sensor calibration. Despite several proven calibration …

We address joint extrinsic calibration of lidar, camera and radar sensors. To simplify
calibration, we propose a single calibration target design for all three modalities, and …

The integration of multiple solid state LiDARs could achieve similar performance as a
spinning LiDAR, by focusing on the dedicated area of interests. However, due to their small …

Autonomous navigation of mobile robots is often based on information from a variety of
heterogeneous sensors; hence, extrinsic sensor calibration is a fundamental step in the …

This work presents an extrinsic parameter estimation algorithm between a 3D LIDAR and a
Projective Camera using a marker-less planar target, by exploiting Planar Surface Point to …

Robust multisensor fusion of multi-modal measurements such as IMUs, wheel encoders,
cameras, LiDARs, and GPS holds great potential due to its innate ability to improve …

Modern autonomous systems often fuse information from many different sensors to enhance
their perception capabilities. For successful fusion, sensor calibration is necessary, while …

This paper proposes SemCal: an automatic, tar-getless, extrinsic calibration algorithm for a
LiDAR and camera system using semantic information. We leverage a neural information …

This work presents a novel target-free extrinsic calibration algorithm for a 3D Lidar and an
IMU pair using an Extended Kalman Filter (EKF) which exploits the motion based calibration …