Eduardo Mario Nebot

Optimization of the simultaneous localization and map-building algorithm for real-time implementation

Addresses real-time implementation of the simultaneous localization and map-building (SLAM) algorithm. It presents optimal algorithms that consider the special form of the matrices and a new compressed filler that can significantly reduce the computation requirements when working in local areas or with high frequency external sensors. It is shown that by extending the standard Kalman filter models the information gained in a local area can be maintained with a cost /spl sim/O(N/sub a//sup 2/), where N/sub a/ is the number of landmarks in the local area, and then transferred to the overall map in only one iteration at full SLAM computational cost. Additional simplifications are also presented that are very close to optimal when an appropriate map representation is used. Finally the algorithms are validated with experimental results obtained with a standard vehicle running in a completely unstructured outdoor environment.

A high integrity IMU/GPS navigation loop for autonomous land vehicle applications

This paper describes the development and implementation of a high integrity navigation system, based on the combined use of the Global Positioning System (GPS) and an inertial measurement unit (IMU), for autonomous land vehicle applications. The paper focuses on the issue of achieving the integrity required of the navigation loop for use in autonomous systems. The paper highlights the detection of possible faults both before and during the fusion process in order to enhance the integrity of the navigation loop. The implementation of this fault detection methodology considers both low frequency faults in the IMU caused by bias in the sensor readings and the misalignment of the unit, and high frequency faults from the GPS receiver caused by multipath errors. The implementation, based on a low-cost, strapdown IMU, aided by either standard or carrier phase GPS technologies, is described. Results of the fusion process are presented.

The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications

This paper presents a new method for improving the accuracy of inertial measurement units (IMUs) mounted on land vehicles. The algorithm exploits nonholonomic constraints that govern the motion of a vehicle on a surface to obtain velocity observation measurements which aid in the estimation of the alignment of the IMU as well as the forward velocity of the vehicle. It is shown that this can be achieved without any external sensing provided that certain observability conditions are met. A theoretical analysis is provided together with a comparison of experimental results between a nonlinear implementation of the algorithm and an IMU/GPS navigation system. This comparison demonstrates the effectiveness of the algorithm. The real time implementation is also addressed through a multiple observation inertial aiding algorithm based on the information filter. The results show that the use of these constraints and vehicle speed guarantees the observability of the velocity and the attitude of the inertial unit, and hence bounds the errors associated with these states. The strategies proposed provides a tighter navigation loop which can sustain outages of GPS for a greater amount of time as compared to when the inertial unit is used with standard integration algorithms.

An evidential approach to map-building for autonomous vehicles

We examine the problem of constructing and maintaining a map of an autonomous vehicles environment for the purpose of navigation, using evidential reasoning. The inherent uncertainty in the origin of measurements of sensors demands a probabilistic approach to processing, or fusing, the new sensory information to build an accurate map. In the paper, the map is based on a two-dimensional (2-D) occupancy grid. The sensor readings are fused into the map using the Dempster-Shafer inference rule. This evidential approach with its multivalued hypotheses allows quantitative analysis of the quality of the data. The map building system is experimentally evaluated using sonar data from real environments.

Consistency of the FastSLAM algorithm

This paper presents an analysis of FastSLAM - a Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping. It shows that the algorithm degenerates with time, regardless of the number of particles used or the density of landmarks within the environment, and would always produce optimistic estimates of uncertainty in the long-term. In essence, FastSLAM behaves like a non-optimal local search algorithm; in the short-term it may produce consistent uncertainty estimates but, in the long-term, it is unable to adequately explore the state-space to be a reasonable Bayesian estimator. However, the number of particles and landmarks does affect the accuracy of the estimated mean and, given sufficient particles, FastSLAM can produce good non-stochastic estimates in practice. FastSLAM also has several practical advantages, particularly with regard to data association, and would probably work well in combination with other versions of stochastic SLAM, such as EKF-based SLAM

An experiment in autonomous navigation of an underground mining vehicle

Describes the theoretical development and experimental evaluation of a navigation system for an autonomous load, haul, and dump truck based on the results obtained during extensive in-situ field trials. The particular contributions of the theoretical work are in designing the navigation system to be able to cope with vehicle slip in rough uneven terrain using information from inertial sensors, odometry, and a bearing only laser. Results are presented using data obtained during the field trials.

Recursive scan-matching SLAM

This paper presents Scan-SLAM, a new generalization of simultaneous localization and mapping (SLAM). SLAM implementations based on extended Kalman filter (EKF) data fusion have traditionally relied on simple geometric models for defining landmarks. This limits EKF-SLAM to environments suited to such models and tends to discard much potentially useful data. The approach presented in this paper is a marriage of EKF-SLAM and scan correlation. Landmarks are no longer defined by analytical models; instead they are defined by templates composed of raw sensed data. These templates can be augmented as more data become available so that the landmark definition improves with time. A new generic observation model is derived that is generated by scan correlation, and this permits stochastic location estimation for landmarks with arbitrary shape within the Kalman filter framework. The statistical advantages of an EKF representation are augmented with the general applicability of scan matching. Scan matching also serves to enhance data association reliability by providing a shape metric for landmark disambiguation. Experimental results in an outdoor environment are presented which validate the algorithm.

Simultaneous localization and map building using natural features and absolute information

Abstract This work presents real time implementation algorithms of Simultaneous Localization and Map Building (SLAM) with emphasis to outdoor land vehicle applications in large environments. It presents the problematic of outdoors navigation in areas with combination of feature and featureless regions. The aspect of feature detection and validation is investigated to reliably detect the predominant features in the environment. Aided SLAM algorithms are presented that incorporate absolute information in a consistent manner. The SLAM implementation uses the compressed filter algorithm to maintain the map with a cost proportional to number of landmarks in the local area. The information gathered in the local area requires a full SLAM update when the vehicle leaves the local area. Algorithms to reduce the full update computational cost are also presented. Finally, experimental results obtained with a standard vehicle running in unstructured outdoor environment are presented.

Real time data association for FastSLAM

The ability to simultaneously localise a robot and accurately map its surroundings is considered by many to be a key prerequisite of truly autonomous robots. This paper presents a real-world implementation of FastSLAM, an algorithm that recursively estimates the full posterior distribution of both robot pose and landmark locations. In particular, we present an extension to FastSLAM that addresses the data association problem using a nearest neighbor technique. Building on this, we also present a novel multiple hypotheses tracking implementation (MHT) to handle uncertainty in the data association. Finally an extension to the multi-robot case is introduced. Our algorithm has been run successfully using a number of data sets obtained in outdoor environments. Experimental results are presented that demonstrate the performance of the algorithms when compared with standard Kalman filter-based approaches.

Data association for mobile robot navigation: a graph theoretic approach

Data association is the process of relating features observed in the environment to features viewed previously or to features in a map. This paper presents a graph theoretic method that is applicable to data association problems where the features are observed via a batch process. Batch observations detect a set of features simultaneously or with sufficiently small temporal difference that, with motion compensation, the features can be represented with precise relative coordinates. This data association method is described in the context of two possible navigation applications: metric map building with simultaneous localisation, and topological map based localisation. Experimental results are presented using an indoor mobile robot with a 2D scanning laser sensor. Given two scans from different unknown locations, the features common to both scans are mapped to each other and the relative change in pose (position and orientation) of the vehicle between the two scans is obtained.