4 accepted papers
We present a novel interval-based visual-inertial LiDAR SLAM (i-VIL SLAM) method that solely assumes sensor errors to be bounded and propagates the error from the input sources to the estimated map and trajectory using interval analysis. The method allows us to restrict the solution set of the robot…
Since cameras and Light Detection and Ranging (LiDAR) sensors provide complementary information about the environment, it is beneficial for mobile robot localization to fuse their information by assigning distances measured by the LiDAR to visual features detected in the image. However, existing app
To fuse information from a 3D Light Detection and Ranging (LiDAR) sensor and a camera, the extrinsic transformation between the sensor coordinate systems needs to be known. Therefore, an extrinsic calibration must be performed, which is usually based on features extracted from sensor data. Naturally…
To properly fuse IMU and camera information for robotics applications, the relative timestamp offset between both sensors' data streams has to be considered. However, finding the exact timestamp offset is often impossible. Thus, it is necessary to additionally consider the offset's uncertainty if we…