Detection and Tracking of Moving Objects Using 2.5D Motion Grids

Transcription

1 Detection and Tracking of Moving Objects Using 2.5D Motion Grids Alireza Asvadi, Paulo Peixoto and Urbano Nunes Institute of Systems and Robotics, University of Coimbra September

2 Outline: Introduction Proposed approach Experimental results Future works 2

3 Introduction 3

4 An intelligent vehicle's modules Sensor inputs Perception Planning Control Actuators o The perception module builds an internal model of the environment using sensor data. o The planning module performs reasoning and makes decisions for future actions based on the current environment's model. o The control module is responsible for translating actions into commands to the vehicle's actuators. 4

5 Some of different aspects of the environment perception for intelligent vehicles 5

6 Environment representation and identifying static and dynamic part of sensor data. o Modeling static part of the environment: Simultaneous Localization And Mapping (SLAM) o Modeling dynamic part of the environment: Detection And Tracking of Moving Objects (DATMO) 6

7 Some recent related work on the perception of dynamic environment surrounding a vehicle. o 2D methods: no volumetric information o 3D methods: high computational cost 7

8 Proposed Approach 8

9 Sensors Velodyne LIDAR INS (GPS/IMU) Perception and vehicle movement measurements To Perception Module Point cloud Localization Update 2.5D grid data Modeling the static part of the environment Local grid map Building local 2.5D grid 2.5D grid Motion detection Moving object detection module List of objects locations Perception Module Kalman tracking Data association Track management Tracking module List of moving objects and tracks To Planning Module o Local 2.5D grid representation o Modeling the static parts o 2.5D grid Motion detection o Moving object detection o Tracking of moving objects 9

10 GPS/IMU localization data Point cloud Update 2.5D grid Modeling the static part of the environment Building local 2.5D grid - 2.5D grid (average height in each cell) - Remove cells that contains points with low variance and height (ground cells). - Local grid mapping: - Keep last n grids (SCGrds). - Use last m (m < n) observations for each cell. - Only consider cells that have been observed for the minimum number of k times. Local grid map Moving object detection module 2.5D grid Motion detection - Generating 2.5D motion grid and spatial reasoning to suppress false detections. - Morphological operators (fill holes and compensate the Velodyne scan s gaps). List of objects locations 10

11 A 2.5D grid stores in each cell of a discrete grid the height of objects above the ground level at the corresponding point of the environment. Building a local 2.5D grid and removing ground points. Along time, the generated grids combined with localization data are integrated into an environment model called local 2.5D map. 11

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13 In every frame, a 2.5D grid is compared with an updated 2.5D map to compute a 2.5D motion grid. Motion grids are grouped to provide an object-level representation of the environment. Some post-processing to unusual size regions and label connected components. The labeled connected components that correspond to moving objects are inputted to the tracking module. 13

14 List of objects and tracks Kalman tracking - 2D Kalman filters - Constant velocity model - One filter for each object Data association Track management - Initialize new tracks for new detected objects. - Remove exited tracks. - Prune tracks. The output is a list of: o Locations of the moving objects List of objects locations - Gating - Nearest neighbor object association Tracking module o 3D bounding boxes of moving objects o Objects tracks 14

15 Experimental Results 15

16 A 2.5D motion grid obtained by simple subtraction of the last grid from local map Result after false Detection suppression Result after grouping, post-processing, and labeling connected components. 16

17 Experimental results on KITTI dataset 17

18 Future Works 18

19 o Improvement of the current work: make the system more robust, less dependent on thresholds assigned empirically, and to assess its performance in real-time applications. o Classification of moving objects. o Static objects should be taken into account. Object detection and classification from static parts of the environment. 19

20 Thank you for your attention 20

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