An Efficient 3D Pedestrian Detector with Calibrated RGB Camera and 3D LiDAR Pedestrian detection plays an important role in the environmental perception and autonomous navigation for robotics, which provides critical information for the safe operation in complex environments. In this paper, a 3D pedestrian detector with calibrated LiDAR and RGB Camera is proposed, which takes full advantage of the precise range information of 3D LiDAR scanner and semantic information acquired from RGB image. The proposed approach integrates the segmented object clusters of point cloud and 2D bounding boxes generated by a visual object detector. The point cloud is segmented by a threestep segmentation approach, which can segment point cloud with both high precision and high efficiency. By fusion multisensor information in the image domain, the proposed approach provides 3D information of pedestrians in both LiDAR and camera coordinate systems. Experiments was conducted to evaluate the performance of the proposed approach.

Automatic Extrinsic Calibration of LIDAR and Monocular Camera Images Mobile robots are being equipped with an ever increasing amount of sensors, and by taking advantage of each modalityxe2x80x99s forte, through sensor fusion we are able to produce superior results for higher level functions such as pedestrian detection. An accurate calibration between the sensors, however, is essential for obtaining a correct fusion. One of the more common setups is that of an optical camera paired with a lidar system, complementing each otherxe2x80x99s data. While the camera offers rich color information through a dense image, the lidar produces high accuracy, though sparse depth measurements. In this paper we will present two methods for estimating the 6 degrees of freedom parameters necessary to extrinsically calibrate the two sensors, without the use of special calibration targets: the first one attempts to align edges detected in both the lidar point cloud and the corresponding color image, while the second approach minimizes the depth difference between the measured lidar data and a depth map generated from the corresponding monocular image. By defining correlations between the two modalityxe2x80x99s information, we are able to construct an optimization problem on the 6 calibration parameters. Our experiments show good results when matched against similar solutions proposed in the literature, especially in the case of the calibration algorithm based on depth estimation.