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With information from multiple input modalities, sensor fusion-basedalgorithms usually out-perform their single-modality counterparts in robotics.Camera and LIDAR, with complementary semantic and depth information, are thetypical choices for detection tasks in complicated driving environments. Formost camera-LIDAR fusion algorithms, however, the calibration of the sensorsuite will greatly impact the performance. More specifically, the detectionalgorithm usually requires an accurate geometric relationship among multiplesensors as the input, and it is often assumed that the contents from thesesensors are captured at the same time. Preparing such sensor suites involvescarefully designed calibration rigs and accurate synchronization mechanisms,and the preparation process is usually done offline. In this work, asegmentation-based framework is proposed to jointly estimate the geometricaland temporal parameters in the calibration of a camera-LIDAR suite. A semanticsegmentation mask is first applied to both sensor modalities, and thecalibration parameters are optimized through pixel-wise bidirectional loss. Wespecifically incorporated the velocity information from optical flow fortemporal parameters. Since supervision is only performed at the segmentationlevel, no calibration label is needed within the framework. The proposedalgorithm is tested on the KITTI dataset, and the result shows an accuratereal-time calibration of both geometric and temporal parameters.

SST-Calib: Simultaneous Spatial-Temporal Parameter Calibration between LIDAR and Camera