Visualinertial SLAM aided estimation of anchor poses and sensor error model parameters of UWB radio modules Local positioning technologies based on ultrawideband (UWB) ranging have become broadly available and accurate enough for various robotic applications. In an infrastructure setup with static anchor radio modules one common problem is to determine their global positions within the world coordinate frame. Furthermore, issues like the complex radiofrequency wave propagation properties make it difficult to design a consistent sensor error model which generalizes well across different anchor setups and environments. Combining radio based local positioning systems with a visualinertial navigation system (VINS) can provide very accurate pose estimates for calibration of the radio based localization modules and at the same time alleviate the inherent drift in visualinertial navigation. We propose an approach to utilize a visualinertial SLAM system using fisheye stereo cameras and an IMU to estimate the anchor 6D poses as well as the parameters of an UWB module sensor error model on a microaerialvehicle (MAV). Fiducial markers on all anchor radio modules are used as artificial landmarks within the SLAM system to get accurate anchor module pose estimates. Index TermsMAVs, mobile robots, SLAM, UWB, radio localization, sensor calibration

Stereo Visual Inertial LiDAR Simultaneous Localization and Mapping Simultaneous Localization and Mapping (SLAM) is a fundamental task to mobile and aerial robotics. LiDAR based systems have proven to be superior compared to vision based systems due to its accuracy and robustness. In spite of its superiority, pure LiDAR based systems fail in certain degenerate cases like traveling through a tunnel. We propose Stereo Visual Inertial LiDAR (VIL) SLAM that performs better on these degenerate cases and has comparable performance on all other cases. VILSLAM accomplishes this by incorporating tightlycoupled stereo visual inertial odometry (VIO) with LiDAR mapping and LiDAR enhanced visual loop closure. The system generates loopclosure corrected 6DOF LiDAR poses in realtime and lcm voxel dense maps near realtime. VILSLAM demonstrates improved accuracy and robustness compared to stateoftheart LiDAR methods.