Bastien Vincke

Real time simultaneous localization and mapping: towards low-cost multiprocessor embedded systems

Simultaneous localization and mapping (SLAM) is widely used by autonomous robots operating in unknown environments. Research community has developed numerous SLAM algorithms in the last 10 years. Several works have presented many algorithms’ optimizations. However, they have not explored a system optimization from the system hardware architecture to the algorithmic development level. New computing technologies (SIMD coprocessors, DSP, multi-cores) can greatly accelerate the system processing but require rethinking the algorithm implementation. This article presents an efficient implementation of the EKF-SLAM algorithm on a multi-processor architecture. The algorithm-architecture adequacy aims to optimize the implementation of the SLAM algorithm on a low-cost and heterogeneous architecture (implementing an ARM processor with SIMD coprocessor and a DSP core). Experiments were conducted with an instrumented platform. Results aim to demonstrate that an optimized implementation of the algorithm, resulting from an optimization methodology, can help to design embedded systems implementing low-cost multiprocessor architecture operating under real-time constraints.

Consistent outdoor vehicle localization by bounded-error state estimation

Localization is a part of many automotive applications where safety is of crucial importance. We think that the best way to guarantee the safety in these applications is to guarantee the results of their embedded localization algorithms. Unfortunately localization of vehicles is mostly solved by Bayesian methods which (due to their structure) can only guarantee their results in a probabilistic way. Interval analysis allows an alternative approach with bounded-error state estimation. Such an approach provides a bounded set of configurations that is guaranteed to surround the actual vehicle configuration. We have validated the bounded-error state estimation with an outdoor vehicle equipped with odometers, a GPS receiver and a gyro. With the experimental results we compare the bounded-error state estimation with the particle filter localization in terms of consistency and computation time.

Design and evaluation of an embedded system based SLAM applications

Simultaneous Localization And Mapping (SLAM) is a branch of algorithms widely used by autonomous robots operating in unknown environments. Research community has developed numerous SLAM algorithms in the last years. Several researches have presented optimizations into different approaches. However, they have not explored a system optimization from the algorithmic development level to the system hardware design. In some applications areas, such as indoor mapping, we would obviously benefit from low-cost sensors technology and SLAM implementations on a smart architecture. In this paper, a solution to the SLAM problem is presented. It is based on the co-design of a hardware architecture, a feature detector, a SLAM algorithm and an optimization methodology. Experiments were conducted with an instrumented vehicle. Results aim to demonstrate that our approach, based on low-cost sensors interfaced to an adequate architecture and an optimized algorithm, is good suitable to design embedded systems for SLAM applications in real time conditions.

A New Local Measure of Disagreement between Belief Functions – Application to Localization

In the theory of belief functions, the disagreement between sources is often measured in terms of conflict or dissimilarity. These measures are global to the sources, and provide few information about the origin of the disagreement. We propose in this paper a “finer” measure based on the decomposition of the global measure of conflict (or distance). It allows focusing the measure on some hypotheses of interest (namely the ones likely to be chosen after fusion).We apply the proposed so called “local” measures of conflict and distance to the choice of sources for vehicle localization.We show that considering sources agreement/disagreement outperforms blind fusion.

Experimental comparison of Bounded-Error State Estimation and Constraints Propagation

The vehicles localization is classically achieved by Bayesian methods like Extended Kalman Filtering. Such methods provide an estimated position with its associated uncertainty. Bounded-error approaches (Bounded-Error State Estimation and Constraints Propagation) use interval analysis and work in a different way as they provide a possible set of positions. An advantage of bounded-error approaches over Bayesian methods is that their results are guaranteed (whereas the results of Bayesian methods are probabilistically defined). This paper compares both Bounded-Error State Estimation and Constraints Propagation using the same experimental data. The results obtained aim to rank these approaches in terms of computing time, consistency and imprecision.

Static and dynamic fusion for outdoor vehicle localization

The vehicles localization is classically achieved by Bayesian methods like Extended Kaiman Filtering. Such a method provides an estimated position with its associated uncertainty. Bounded-error approaches using interval analysis work in a different way as they provide a possible set of positions. An advantage of such approaches is that the results are guaranteed and are not probabilistically defined. This paper focuses on constraints propagation techniques using static and dynamic fusion. Static fusion uses data redundancy to enhance proprioceptive data. Then dynamic fusion uses GPS in order to reduce the size of the localization box. The approach has been validated with a real outdoor vehicle.

Graph-based SLAM embedded implementation on low-cost architectures: A practical approach

The graph-based SLAM (Simultaneous Localization and Mapping) method uses a graph to represent and solve the SLAM problem. The SLAM allows building a map of an unknown environment and simultaneously localizing the robot on this map. This paper presents a temporal analysis of the 3D graph-based SLAM method. We also propose an efficient implementation, on an OMAP embedded architecture, which is a widely used open multimedia applications platform. We provide an optimized data structure and an efficient memory access management to solve the nonlinear least squares problem related to the algorithm. The algorithm takes advantage of the Schur complement to reduce the execution time. We will present an optimized implementation of this task. We also take advantage of the multi-core architecture to parallelize the algorithm. To evaluate our implementation, we will compare the computational performances to the well known framework g2o. This work aims to demonstrate how optimizing data structure and multi-threading can decrease significantly the execution time of the graph-based SLAM on a low-cost architecture dedicated to embedded applications.

A Practical Approach for High Precision Reconstruction of a Motorcycle Trajectory Using a Low-Cost Multi-Sensor System

Motorcycle drivers are considered among the most vulnerable road users, as attested by the number of crashes increasing every year. The significant part of the fatalities relates to “single vehicle” loss of control in bends. During this investigation, a system based on an instrumented multi-sensor platform and an algorithmic study was developed to accurately reconstruct motorcycle trajectories achieved when negotiating bends. This system is used by the French Gendarmerie in order to objectively evaluate and to examine the way riders take their bends in order to better train riders to adopt a safe trajectory and to improve road safety. Data required for the reconstruction are acquired using a motorcycle that has been fully instrumented (in VIROLO++ Project) with several redundant sensors (reference sensors and low-cost sensors) which measure the rider actions (roll, steering) and the motorcycle behavior (position, velocity, acceleration, odometry, heading, and attitude). The proposed solution allowed the reconstruction of motorcycle trajectories in bends with a high accuracy (equal to that of fixed point positioning). The developed algorithm will be used by the French Gendarmerie in order to objectively evaluate and examine the way riders negotiate bends. It will also be used for initial training and retraining in order to better train riders to learn and estimate a safe trajectory and to increase the safety, efficiency and comfort of motorcycle riders.

Speeding up graph-based SLAM algorithm: A GPU-based heterogeneous architecture study

In this paper we present a study of using an heterogeneous architecture to implement the graph-based SLAM algorithm. The study aims to investigate the performances of an ARM-GPU based architecture by offloading some critical compute-intensive tasks of the algorithm to the integrated GPU.

Guaranteed simultaneous localization and mapping algorithm using interval analysis

To increase the autonomy of robots, it is necessary to have a precise and guaranteed localization. We propose a SLAM algorithm based on interval analysis and constraints propagation. The problem is casted into a constraint satisfaction which is solved in a guaranteed way via Interval Analysis. We define a new bounded landmark parameterization and an initialization method for monocular camera. Finally, we introduce the post-localization process which improves the localization accuracy using the future observations. Both simulations and experiments show guaranteed and consistent results of CP-SLAM.