Abstract
Obstacle avoidance and available road identification technologies have been investigated for autonomous driving of an unmanned vehicle. In order to apply research results to autonomous driving in real environments, it is necessary to consider moving objects. This article proposes a preprocessing method to identify the dynamic zones where moving objects exist around an unmanned vehicle. This method accumulates three-dimensional points from a light detection and ranging sensor mounted on an unmanned vehicle in voxel space. Next, features are identified from the cumulative data at high speed, and zones with significant feature changes are estimated as zones where dynamic objects exist. The approach proposed in this article can identify dynamic zones even for a moving vehicle and processes data quickly using several features based on the geometry, height map and distribution of three-dimensional space data. The experiment for evaluating the performance of proposed approach was conducted using ground truth data on simulation and real environment data set.
Highlights
Intelligent robots or unmanned vehicles that can identify surrounding conditions and perform using recent sensor development have been actively investigated
This article proposed a preprocessing method for detecting zones containing dynamic objects in a cumulative voxel environment
This article estimated zones with dynamic objects by identifying the features of voxels corresponding to the traces made by dynamic objects in a cumulative voxel environment without segmenting the ground and objects
Summary
Intelligent robots or unmanned vehicles that can identify surrounding conditions and perform using recent sensor development have been actively investigated. Previous research has applied object segmentation and object tracking technology for detecting dynamic objects in such environments.[8,9,13] Object segmentation executes clustering 3D points for each object on the basis of the correlation among 3D points acquired from a sensor This process is executed slowly because it requires a quantity of computing resources owing to redundant searching of 3D points. To improve the processing rate, it is necessary to reduce the number of redundant searches of 3D points This can be done by executing object segmentation and tracking only in zones which are estimated to have dynamic objects.
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