Denis F. Wolf

Mobile Robot Simultaneous Localization and Mapping in Dynamic Environments

We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two grid maps provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and real robots experiments show the efficiency of our approach and also show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.

Adaptive Teams of Autonomous Aerial and Ground Robots for Situational Awareness

This is a preprint of an article accepted for publication in the Journal of Field Robotics, copyright 2007. Journal of Field Robotics 24(11), 991–1014 (2007)

Towards 3D mapping in large urban environments

This paper describes work-in-progress aimed at generating dense 3D maps of urban environments using laser range data acquired from a moving platform. These maps display both fine-scale detail (resolving features only a few centimeters across) and large-scale consistency (typical maps are approximately 0.5 km on a side). In this paper, we sketch a basic 3D mapping algorithm (paying particular attention to practical engineering details) and present preliminary results acquired on the USC University Park campus using a Segway RMP vehicle.

Semantic Mapping Using Mobile Robots

Robotic mapping is the process of automatically constructing an environment representation using mobile robots. We address the problem of semantic mapping, which consists of using mobile robots to create maps that represent not only metric occupancy but also other properties of the environment. Specifically, we develop techniques to build maps that represent activity and navigability of the environment. Our approach to semantic mapping is to combine machine learning techniques with standard mapping algorithms. Supervised learning methods are used to automatically associate properties of space to the desired classification patterns. We present two methods, the first based on hidden Markov models and the second on support vector machines. Both approaches have been tested and experimentally validated in two problem domains: terrain mapping and activity-based mapping.

Online simultaneous localization and mapping in dynamic environments

We propose an on-line algorithm for simultaneous localization and mapping of dynamic environments. Our algorithm is capable of differentiating static and dynamic parts of the environment and representing them appropriately on the map. Our approach is based on maintaining two occupancy grids. One grid models the static parts of the environment, and the other models the dynamic parts of the environment. The union of the two provides a complete description of the environment over time. We also maintain a third map containing information about static landmarks detected in the environment. These landmarks provide the robot with localization. Results in simulation and with physical robots show the efficiency of our approach and show how the differentiation of dynamic and static entities in the environment and SLAM can be mutually beneficial.

Mobile Robots Navigation in Indoor Environments Using Kinect Sensor

This paper presents the development of a perception system for indoor environments to allow autonomous navigation for surveillance mobile robots. The system is composed by two parts. The first part is a reactive navigation system in which a mobile robot moves avoiding obstacles in environment, using the distance sensor Kinect. The second part of this system uses a artificial neural network (ANN) to recognize different configurations of the environment, for example, path ahead, left path, right path and intersections. The ANN is trained using data captured by the Kinect sensor in indoor environments. This way, the robot becomes able to perform a topological navigation combining internal reactive behavior to avoid obstacles and the ANN to locate the robot in the environment, in a deliberative behavior. The topological map is represented by a graph which represents the configuration of the environment, where the hallways (path ahead) are the edges and locations (left path and intersection, for example) are the vertices. The system also works in the dark, which is a great advantage for surveillance systems. The experiments were performed with a Pioneer P3-AT robot equipped with a Kinect sensor in order to validate and evaluate this approach. The proposed method demonstrated to be a promising approach to autonomous mobile robots navigation.

CaRINA Intelligent Robotic Car: Architectural design and applications

Abstract This paper presents the development of two outdoor intelligent vehicles platforms named CaRINA I and CaRINA II, their system architecture, simulation tools, and control modules. It also describes the development of the intelligent control system modules allowing the mobile robots and vehicles to navigate autonomously in controlled urban environments. Research work has been carried out on tele-operation, driver assistance systems, and autonomous navigation using the vehicles as platforms to experiments and validation. Our robotic platforms include mechanical adaptations and the development of an embedded software architecture. This paper addresses the design, sensing, decision making, and acting infrastructure and several experimental tests that have been carried out to evaluate both platforms and proposed algorithms. The main contributions of this work is the proposed architecture, that is modular and flexible, allowing it to be instantiated into different robotic platforms and applications. The communication and security aspects are also investigated.

Robust curb detection and vehicle localization in urban environments

Curb detection is an important capability for autonomous ground vehicles in urban environments. It is particularly useful for path planning and safe navigation. Another important task that can benefit from curb detection is localization, which is also a major requirement for self-driving cars. There are several approaches for identifying curbs using stereo cameras and 2D LIDARs in the literature. Stereo cameras depend on image pair matching methods to obtain depth information. Although 2D LIDARs being able to directly return this information, only few curb points can be detected using this sensor. In this work we propose the use of a 3D LIDAR which provides a dense point cloud and thus make possible to detect a larger extent of the curb. Our approach introduces the use of robust regression method named least trimmed squares (LTS) to deal with occluding scenes in contrast of temporal filters and spline fitting methods. We also used the curb detector as an input of a Monte Carlo localization algorithm, which is capable to estimate the pose of the vehicle without an accurate GPS sensor. We conducted experiments in urban environments to validate both the curb detector and the localization algorithm. Both method delivered successful results in different traffic situations and an average lateral localization error of 0.52655 m in a 800 m track.

Towards geometric 3D mapping of outdoor environments using mobile robots

This paper presents an approach to generating compact 3D maps of urban environments using mobile robots and laser range finders. Our algorithm extracts planar information from 3D point cloud maps. The planar representation is very efficient for representing building structures in urban environments when a high level of detail is not required. We also present preliminary results on 3D geometric mapping with incomplete data. Based on previously known models and incomplete data, our system is able to estimate parts of buildings which have never been seen before. As validation, we present experimental results using a Segway RMP vehicle in two environments, both approximately the size of a city block.

Fast visual road recognition and horizon detection using multiple artificial neural networks

The development of autonomous vehicles is a highly relevant research topic in mobile robotics. Road recognition using visual information is an important capability for autonomous navigation in urban environments. Over the last three decades, a large number of visual road recognition approaches have been appeared in the literature. This paper proposes a novel visual road detection system based on multiple artificial neural networks that can identify the road based on color and texture. Several features are used as inputs of the artificial neural network such as: average, entropy, energy and variance from different color channels (RGB, HSV, YUV). As a result, our system is able to estimate the classification and the confidence factor of each part of the environment detected by the camera. Experimental tests have been performed in several situations in order to validate the proposed approach.