Terrain Classification Techniques From Ladar Data For Autonomous Navigation

Autonomous navigation remains a considerable challenge, primarily because of the difficulty in describing the environment of the robot in a way that captures the variability of natural environments. In this paper, we focus on the problem of extracting the ground terrain surface from sparse 3-D data from LADAR mobility sensors, including the segmentation of the terrain from obscuring vegetation. In this paper, we briefly review possible approaches to LADAR processing, discuss their limitations, and describe our current approach. Results obtained with the GDRS CTA LADAR are presente

Quality Assessment of Traversability Maps from Aerial LIDAR Data for an Unmanned Ground Vehicle

In this paper we address the problem of assessing quantitatively the quality of traversability maps computed from data collected by an airborne laser range finder. Such data is used to plan paths for an unmanned ground vehicle (UGV) prior to the execution of long range traverses. Little attention has been devoted to the problem we address in this paper. We use a unique data set of geodetic control points, real robot navigation data, ground LIDAR (LIght Detection And Ranging) data and aerial imagery, collected during a week long demonstration to support our work

Directional Associative Markov Network for 3-D Point Cloud Classification

In this paper we address the problem of automated three dimensional point cloud interpretation. This problem is important for various tasks from environment modeling to obstacle avoidance for autonomous robot navigation. In addition to locally extracted features, classifiers need to utilize contextual information in order to perform well. A popular approach to account for context is to utilize the Markov Random Field framework. One recent variant that has successfully been used for the problem considered is the Associative Markov Network (AMN). We extend the AMN model to learn directionality in the clique potentials, resulting in a new anisotropic model that can be efficiently learned using the subgradient method. We validate the proposed approach using data collected from different range sensors and show better performance against standard AMN and Support Vector Machine algorithms

Analysis and Removal of Artifacts in 3-D LADAR Data

Errors in laser based range measurements can be divided into two categories: intrinsic sensor errors (range drift with temperature, systematic and random errors), and errors due to the interaction of the laser beam with the environment. The former have traditionally received attention and can be modeled. The latter in contrast have long been observed but not well characterized. We propose to do so in this paper. In addition, we present a sensor independent method to remove such artifacts. The objective is to improve the overall quality of 3-D scene reconstruction to perform terrain classification of scenes with vegetation

Onboard Contextual Classification of 3-D Point Clouds with Learned High-order Markov Random Fields

Contextual reasoning through graphical models such as Markov Random Fields often show superior performance against local classifiers in many domains. Unfortunately, this performance increase is often at the cost of time consuming, memory intensive learning and slow inference at testing time. Structured prediction for 3-D point cloud classification is one example of such an application. In this paper we present two contributions. First we show how efficient learning of a random field with higher-order cliques can be achieved using subgradient optimization. Second, we present a context approximation using random fields with high-order cliques designed to make this model usable online, onboard a mobile vehicle for environment modeling. We obtained results with the mobile vehicle on a variety of terrains, at 1/3 Hz for a map 25x 50 meters and a vehicle speed of 1-2 m/s

Experimental Results in Using Aerial LADAR Data for Mobile Robot Navigation

In this paper, we investigate the use of high resolution aerial LADAR data for autonomous mobile robot navigation in natural environments. The use of prior maps from aerial LADAR (LAser Detection And Ranging) survey is considered for enhancing system performance in two areas. First, the prior maps are used for registration with the data from the robot in order to compute accurate localization in the map. Second, the prior maps are used for computing detailed traversability maps that are used for planning over long distances. Our objective is to assess the key issues in using such data and to report on a first batch of experiments in combining high-resolution aerial data and on-board sensin

Data Structures for Efficient Dynamic Processing in 3-D

In this paper, we consider the problem of the dynamic processing of large amounts of sparse three-dimensional data. It is assumed that computations are performed in a neighborhood defined around each point in order to retrieve local properties. This general kind of processing can be applied to a wide variety of problems. We propose a new, efficient data structure and corresponding algorithms that significantly improve the speed of the range search operation and that are suitable for on-line operation where data is accumulated dynamically. The method relies on taking advantage of overlapping neighborhoods and the reuse of previously computed data as the algorithm scans each data point. To demonstrate the dynamic capabilities of the data structure, we use data obtained from a laser radar mounted on a ground mobile robot operating in complex, outdoor environments. We show that this approach considerably improves the speed of an established 3-D perception processing algorithm

Classifier Fusion for Outdoor Obstacle Detection

This work describes an approach for using several levels of data fusion in the domain of autonomous off-road navigation. We are focusing on outdoor obstacle detection, and we present techniques that leverage on data fusion and machine learning for increasing the reliability of obstacle detection systems. We are combining color and infrared (IR) imagery with range information from a laser range finder. We show that in addition to fusing data at the pixel level, performing high level classifier fusion is beneficial in our domain. Our general approach is to use machine learning techniques for automatically deriving effective models of the classes of interest (obstacle and non-obstacle for example). We train classifiers on different subsets of the features we extract from our sensor suite and show how different classifier fusion schemes can be applied for obtaining a multiple classifier system that is more robust than any of the classifiers presented as input. We present experimental results we obtained on data collected with both the experimental unmanned vehicle (XUV) and a CMU developed robotic tractor

Toward Laser Pulse Waveform Analysis for Scene Interpretation

Laser based sensing for scene interpretation and obstacle detection is challenged by partially viewed targets, wiry structures, and porous objects. We propose to address such problems by looking at the laser pulse waveform. We designed a new laser sensor with off-the-shelf components. In this paper we report on the design and the evaluation of this low cost and compact sensor, suitable for mobile robot application. We determine classical parameters such as operation range, repeatability, accuracy, resolution, but we also analyze laser pulse waveforms modes and mode shape in order to extract additional information on the scene

Unmanned Ground Vehicle Navigation Using Aerial Ladar Data

In this paper, we investigate the use of overhead high-resolution three-dimensional (3-D) data for enhancing the performances of an Unmanned Ground Vehicle (UGV) in vegetated terrains. Data were collected using an airborne laser and provided prior to the robot mission. Through extensive and exhaustive field testing, we demonstrate the significance of such data in two areas: robot localization and global path planning. Absolute localization is achieved by registering 3-D local ground ladar data with the global 3-D aerial data. The same data is used to compute traversability maps that are used by the path planner. Vegetation is filtered both in the ground data and in the aerial data in order to recover the load bearing surface