Tremendous Research work is devoted to advancing state of art in intelligent transportation systems, with the ultimate objective of maximizing the beneficial effect of the transportation sector on the environment. One of the most critical parts of balancing energy consumption and resource exploitation in sustainable development is the introduction of decarbonized transportation in the form of electric cars. This article discusses the results of a study that evaluates the energy needed to power a battery electric vehicle (BEV) when the driver switches to the comfort ride option. We have modeled a Battery electric vehicle, thermal management system, and Adaptive Cruise (ACC) blocks using MATLAB/Simulink. To evaluate our developed model, we have conducted simulation tests under a variety of settings, including route and temperature data from Moroccan roads.

In this work, we investigate the accuracy of attitude estimation solutions based on inertial/magnetic sensors in the context of motorcycle trajectory reconstruction. We propose a comparative study and analysis of a number of quaternion based attitude-filtering methods. We conduct an experimental study using a multi-sensor platform with a precise ground truth obtained with a GPS RTK. We quantify the error in the motorcycle trajectory estimation obtained with each method and we discuss the obtained results.

This paper deals simultaneously with the trajectory estimation and map reconstruction by means of a stereo-calibrated vision system evolving in a large-scale unknown environment. This problem is widely known as Visual SLAM. Our proposal optimizes the execution time of the VSLAM framework while preserving its localization accuracy. The contributions of this paper are structured as follows. First, a novel VSLAM approach based on a “Weighted Mean” of multiple neighbor poses is detailed and is denoted as HOOFR SLAM. This approach provides a localization estimate after computing the camera poses (6-DOF rigid transformation) from the current image frame to previous neighbor frames. Taking advantage of the camera motion, we conjointly incorporate two types of stereo modes: “Static Stereo” mode (SS) through the fixed-baseline of left-right cameras setup along with the “Temporal Multi-view Stereo” mode (TMS). Moreover, instead of computing beforehand the disparity of SS mode for all key-points set, the disparity map in scale estimation step is limited to the inliers of the TMS mode so as to reduce the computational cost. This strategy is suitable to be parallelized on a multiprocessor architecture and exhibits a competitive performance with the other state-of-the-art strategies in many real datasets. Second, we report a hardware-software mapping of the proposed VSLAM approach. To this end, a heterogeneous CPU-GPU architecture-based vision system is considered. Third, a thorough and extensive experimental evaluation of our algorithm implemented on an automotive architecture (the NVIDIA Tegra TX1 system) is studied and analyzed. We report hence the localization and timing results through experiments on five well-known public stereo SLAM datasets: KITTI, Malaga, Oxford, MRT, and St_Lucia datasets.

The development of SLAM algorithms in the era of autonomous navigation and the growing demand for autonomous robot in place of human being, has put into question how to reduce the computational complexity and make use of these algorithms to operate in real time. Our work aims to take advantage of the high level synthesis (HLS) on FPGAs to design a real time SLAM application. Precisely, we evaluate the promess-held by the new modern low power FPGAs in accelerating SLAM algorithms. Throughtout this, we will attempt to implement a well-known algorithms (FastSLAM2.0), on a new modern FPGA using OpenCL, a standard high level language. Our implementation results show a significant improvement of the algorithm processing time on an FPGA device over a modern powerful embedded GPGPU.

In this paper, we propose an algorithm called Hessian ORB — Overlapped FREAK (HOOFR) to attack the feature matching problem in image processing. Our algorithm is based on the combination of the ORB detector and the FREAK bio-inspired descriptor. We address some modifications related to the detection and the description processes in order to enhance HOOFR reliability, speed and memory fingerprint. The experiments on a widely used dataset demonstrate the considerable performance of HOOFR compared to SIFT, SURF or ORB in terms of the execution time and the matching quality for various matching contexts. Moreover, the performance is also tested by integrating HOOFR in object-tracking application on ODROID-XU4 platform, presenting a high potential of its implementation on embedded systems.

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Feature matching plays an important role in many computer vision applications, such as object recognition, scene reconstruction or image mosaicing. In this paper, we propose an algorithm called Hessian ORB - Overlapped FREAK (HOOFR) which is based on the combination of the ORB detector and the FREAK bio-inspired descriptor. We address some modifications related to the detection and the description processes in order to enhance HOOFR reliability, speed and memory fingerprint. The experiments on a widely used dataset demonstrate the considerable performance of HOOFR compared to SIFT, SURF or ORB in terms of the execution time and the matching quality, in various matching contexts.

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.

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.

The first method that was developed to deal with the SLAM problem is based on the extended Kalman filter, EKF SLAM. However this approach cannot be applied to a large environments because of the quadratic complexity and data association problem. The second approach to address the SLAM problem is based on the Rao-Blackwellized Particle filter FastSLAM, which follows a large number of hypotheses that represent the different possible trajectories, each trajectory carries its own map, its complexity increase logarithmically with the number of landmarks in the map. In this paper we will present the result of an implementation of the FastSLAM 2.0 on an open multimedia applications processor, based on a monocular camera as an exteroceptive sensor. A parallel implementation of this algorithm was achieved. Results aim to demonstrate that an optimized algorithm implemented on a low cost architecture is suitable to design an embedded system for SLAM applications.

An autonomous robot has to localize itself in an unknown area. Simultaneous Localization and Mapping (SLAM) allows for a robot to build a map of an unknown environment and localize simultaneously itself on this map. Graph-based SLAM methods use a graph to represent and solve the SLAM problem. This paper presents an optimized implementation of the incremental 3D graph-based SLAM on an OMAP architecture used as open multimedia applications platform. This implementation uses an optimized data structure and an efficient memory access management to solve the nonlinear least squares problem related to the algorithm. It takes also advantage of the multi-core architecture to parallelize the algorithm. To evaluate our implementation, we will evaluate the processing times of the implemented algorithm compared to those of the well known framework g2o.

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.

This paper describes a multi-FPGA architecture dedicated for the real-time imaging using the Total Focusing Method (TFM) and an advanced acquisition technique called the Full Matrix Capture (FMC). The maximum operating frequency is 147.3 MHz for the control blocks and 161.3 MHz for the acquisition blocks. The architecture is able to perform real-time image reconstruction at a maximum frame rate of 73 fps and a maximum resolution of 128 × 128 pixels, which is adequate as a performance for a real-time imaging system with a good characterization of defects.

Research community has developed numerous SLAM (Simultaneous Localization And Mapping) algorithms in the last ten years. These algorithms are widely used by autonomous robots operating in unknown environments. Several works have presented many algorithms' optimizations. New computing technologies (SIMD coprocessors, multi-cores) can greatly accelerate the system processing but require rethinking the algorithm implementation. This paper presents an efficient implementation of the EKF-SLAM algorithm on a multicore architecture. The algorithm-architecture adequacy aims to optimize the implementation of the SLAM algorithm on a low-cost architecture (Dual-core and Dual SIMD coprocessor). Experiments were conducted with an instrumented platform. Results aim to demonstrate that an optimized implementation of the algorithm, resulting from an evaluation methodology, can help to design embedded systems implementing low-cost multicore architecture operating under real time constraints.

The vehicle's 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.