Pedestrian Intensive Scanning for Active-scan LIDAR

In recent years, LIDAR is playing an important role as a sensor for understanding environments of a vehicle’s surroundings. Active-scan LIDAR is being actively developed as a LIDAR that can control the laser irradiation direction arbitrary and rapidly. In comparison with conventional uniform-scan LIDAR (e.g. Velodyne HDL-64e), Active-scan LIDAR enables us to densely scan even distant pedestrians. In addition, if appropriately controlled, this sensor has a potential to reduce unnecessary laser irradiations towards non-target objects. Although there are some preliminary studies on pedestrian scanning strategy for Active-scan LIDARs, in the best of our knowledge, an efficient method has not been realized yet. Therefore, this paper proposes a novel pedestrian scanning method based on orientation aware pedestrian likelihood estimation using the orientation-wise pedestrian’s shape models with local distribution of measured points. To evaluate the effectiveness of the proposed method, we conducted experiments by simulating Active-scan LIDAR using point-clouds from the KITTI dataset. Experimental results showed that the proposed method outperforms the conventional methods.

[1]  Hiroshi Murase,et al.  Pedestrian detection from sparse point-cloud using 3DCNN , 2018, 2018 International Workshop on Advanced Image Technology (IWAIT).

[2]  Armin B. Cremers,et al.  Laser-based segment classification using a mixture of bag-of-words , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  Yin Zhou,et al.  VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Hiroshi Murase,et al.  Efficient pedestrian scanning by active scan LIDAR , 2018, 2018 International Workshop on Advanced Image Technology (IWAIT).

[5]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  L. Devroye Non-Uniform Random Variate Generation , 1986 .

[7]  Heng Wang,et al.  Robotics and Autonomous Systems , 2022 .

[8]  Jun Miura,et al.  Pedestrian Recognition Using High-definition LIDAR , 2011 .

[9]  Sebastian Scherer,et al.  VoxNet: A 3D Convolutional Neural Network for real-time object recognition , 2015, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).