Traversable Ground Research Articles

TRAVEL: Traversable Ground and Above-Ground Object Segmentation Using Graph Representation of 3D LiDAR Scans

Perception of traversable regions and objects of interest from a 3D point cloud is one of the critical tasks in autonomous navigation. A ground vehicle needs to look for traversable terrains that are explorable by wheels. Then, to make safe navigation decisions, the segmentation of objects positioned on those terrains has to be followed up. However, over-segmentation and under-segmentation can negatively influence such navigation decisions. To that end, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TRAVEL</i> , which performs traversable ground detection and object clustering simultaneously using the graph representation of a 3D point cloud. To segment the traversable ground, a point cloud is encoded into a graph structure, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">tri-grid field</i> , which treats each tri-grid as a node. Then, the traversable regions are searched and redefined by examining local convexity and concavity of edges that connect nodes. On the other hand, our above-ground object segmentation employs a graph structure by representing a group of horizontally neighboring 3D points in a spherical-projection space as a node and vertical/horizontal relationship between nodes as an edge. Fully leveraging the node-edge structure, the above-ground segmentation ensures real-time operation and mitigates over-segmentation. Through experiments using simulations, urban scenes, and our own datasets, we have demonstrated that our proposed traversable ground segmentation algorithm outperforms other state-of-the-art methods in terms of the conventional metrics and that our newly proposed evaluation metrics are meaningful for assessing the above-ground segmentation. We make the code and our own dataset available to public at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/url-kaist/TRAVEL</uri> .

A Riemannian approach for free-space extraction and path planning using catadioptric omnidirectional vision

This paper presents a Riemannian approach for free-space extraction and path planning using color catadioptric vision. The problem is formulated considering color catadioptric images as Riemannian manifolds and solved using the Riemannian Eikonal equation with an anisotropic fast marching numerical scheme. This formulation allows the integration of adapted color and spatial metrics in an incremental process. First, the traversable ground (namely free-space) is delimited using a color structure tensor built on the multi-dimensional components of the catadioptric image. Then, the Eikonal equation is solved in the image plane incorporating a generic metric tensor for central catadioptric systems. This built Riemannian metric copes with the geometric distortions in the catadioptric image plane introduced by the curved mirror in order to compute the geodesic distance map and the shortest path between image points. We present comparative results using Euclidean and Riemannian distance transforms and show the effectiveness of the Riemannian approach to produce safest path planning.

Air-ground cooperative topometric mapping of traversable ground

In this paper, we propose an approach for cooperative mapping of traversable ground from aerial and ground views in structured outdoor and indoor environments. The presented approach achieves a hybrid map building based on traversable ground skeletonization and graph matching. The obtained map is an augmented ground traversability map, represented as a hybrid topological/metric graph from heterogeneous sources. This approach provides a very suitable representation for ground navigation and planning. To validate this approach, the proposed algorithm is applied between aerial views, provided by a UAV flying over an experimental site, and ground maps from ground robots at different exploration stages, in realistic simulation and real-world environments.

Adaptive ground segmentation method for real-time mobile robot control

For an autonomous mobile robot operating in an unknown environment, distinguishing obstacles from the traversable ground region is an essential step in determining whether the robot can traverse th...

LIDAR and stereo combination for traversability assessment of off-road robotic vehicles

SUMMARYReliable assessment of terrain traversability using multi-sensory input is a key issue for driving automation, particularly when the domain is unstructured or semi-structured, as in natural environments. In this paper, LIDAR-stereo combination is proposed to detect traversable ground in outdoor applications. The system integrates two self-learning classifiers, one based on LIDAR data and one based on stereo data, to detect the broad class of drivable ground. Each single-sensor classifier features two main stages: an adaptive training stage and a classification stage. During the training stage, the classifier automatically learns to associate geometric appearance of 3D data with class labels. Then, it makes predictions based on past observations. The output obtained from the single-sensor classifiers are statistically combined in order to exploit their individual strengths and reach an overall better performance than could be achieved by using each of them separately. Experimental results, obtained with a test bed platform operating in rural environments, are presented to validate and assess the performance of this approach, showing its effectiveness and potential applicability to autonomous navigation in outdoor contexts.

Studies on Microjet Control Effectiveness in High-Temperature Supersonic Impinging Jets

This study examines the influence of jet temperature on the impinging jet flowfield and compares the effectiveness of microjet control for a Mach 1.5 ideally expanded jet. The tests were performed on a 36.2-mm-diam jet of air, heated to stagnation temperatures up to 1200 K. The jet impinged normally onto a traversable ground plane oriented between 1.5 and 12 throat diameters from the nozzle exit. Findings indicate that the impingement tones persist and generally become more prominent at higher temperatures, and the lift loss levels increase. Microjet injection produced substantial reductions of overall noise levels and considerable lift recovery, although the effectiveness is slightly lower for elevated jet temperatures. In the nozzle-to-ground range resulting in the highest pressure fluctuation levels on the lift plate, reductions reached 9 dB for the 1200 K jet and 10 dB for the cold jet. There was consistent behavior regarding the suppression of near-field and far-field overall sound pressure levels, with maximum reductions for the 811 K jet of 7 and 8 dB, respectively. The lift loss at small nozzle-to-ground distances for elevated jet temperatures increased by up to 30% compared to the cold jet, and microjets were able to reduce lift loss by as much as 34% for the 811 K jet.

Traversable Ground Surface Segmentation and Modeling for Real-Time Mobile Mapping

Remote vehicle operator must quickly decide on the motion and path. Thus, rapid and intuitive feedback of the real environment is vital for effective control. This paper presents a real-time traversable ground surface segmentation and intuitive representation system for remote operation of mobile robot. Firstly, a terrain model using voxel-based flag map is proposed for incrementally registering large-scale point clouds in real time. Subsequently, a ground segmentation method with Gibbs-Markov random field (Gibbs-MRF) model is applied to detect ground data in the reconstructed terrain. Finally, we generate a texture mesh for ground surface representation by mapping the triangles in the terrain mesh onto the captured video images. To speed up the computation, we program a graphics processing unit (GPU) to implement the proposed system for large-scale datasets in parallel. Our proposed methods were tested in an outdoor environment. The results show that ground data is segmented effectively and the ground surface is represented intuitively.

3D reconstruction and classification of natural environments by an autonomous vehicle using multi-baseline stereo

In natural outdoor settings, advanced perception systems and learning strategies are major requirement for an autonomous vehicle to sense and understand the surrounding environment, recognizing artificial and natural structures, topology, vegetation and drivable paths. Stereo vision has been used extensively for this purpose. However, conventional single-baseline stereo does not scale well to different depths of perception. In this paper, a multi-baseline stereo frame is introduced to perform accurate 3D scene reconstruction from near range up to several meters away from the vehicle. A classifier that segments the scene into navigable and non-navigable areas based on 3D data is also described. It incorporates geometric features within an online self-learning framework to model and identify traversable ground, without any a priori assumption on the terrain characteristics. The ground model is automatically retrained during the robot motion, thus ensuring adaptation to environmental changes. The proposed strategy is of general applicability for robot’s perception and it can be implemented using any range sensor. Here, it is demonstrated for stereo-based data acquired by the multi-baseline device. Experimental tests, carried out in a rural environment with an off-road vehicle, are presented. It is shown that the use of a multi-baseline stereo frame allows for accurate reconstruction and scene segmentation at a wide range of visible distances, thus increasing the overall flexibility and reliability of the perception system.