Once limited to the military domain, unmanned aerial vehicles are now poised to gain widespread adoption in the commercial sector. One such application is to deploy these aircraft, also known as drones, for last-mile delivery in logistics operations. While significant research efforts are underway to improve the technology required to enable delivery by drone, less attention has been focused on the operational challenges associated with leveraging this technology. This paper provides two mathematical programming models aimed at optimal routing and scheduling of unmanned aircraft, and delivery trucks, in this new paradigm of parcel delivery. In particular, a unique variant of the classical vehicle routing problem is introduced, motivated by a scenario in which an unmanned aerial vehicle works in collaboration with a traditional delivery truck to distribute parcels. We present mixed integer linear programming formulations for two delivery-by-drone problems, along with two simple, yet effective, heuristic solution approaches to solve problems of practical size. Solutions to these problems will facilitate the adoption of unmanned aircraft for last-mile delivery. Such a delivery system is expected to provide faster receipt of customer orders at less cost to the distributor and with reduced environmental impacts. A numerical analysis demonstrates the effectiveness of the heuristics and investigates the tradeoffs between using drones with faster flight speeds versus longer endurance.

The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery

Perception system design is a vital step in the development of an autonomous vehicle (AV). With the vast selection of available off-the-shelf schemes and seemingly endless options of sensor systems implemented in research and commercial vehicles, it can be difficult to identify the optimal system for one’s AV application. This article presents a comprehensive review of the state-of-the-art AV perception technology available today. It provides up-to-date information about the advantages, disadvantages, limits, and ideal applications of specific AV sensors; the most prevalent sensors in current research and commercial AVs; autonomous features currently on the market; and localization and mapping methods currently implemented in AV research. This information is useful for newcomers to the AV field to gain a greater understanding of the current AV solution landscape and to guide experienced researchers towards research areas requiring further development. Furthermore, this paper highlights future research areas and draws conclusions about the most effective methods for AV perception and its effect on localization and mapping. Topics discussed in the Perception and Automotive Sensors section focus on the sensors themselves, whereas topics discussed in the Localization and Mapping section focus on how the vehicle perceives where it is on the road, providing context for the use of the automotive sensors. By improving on current state-of-the-art perception systems, AVs will become more robust, reliable, safe, and accessible, ultimately providing greater efficiency, mobility, and safety benefits to the public.

Autonomous vehicle perception: The technology of today and tomorrow

Integrity is one criteria to evaluate GNSS performance, which was first introduced in the aviation field. It is a measure of trust which can be placed in the correctness of the information supplied by the total system. In recent years, many GNSS-based applications emerge in the urban environment including liability critical ones, so the concept of integrity attracts more and more attention from urban GNSS users. However, the algorithms developed for the aerospace domain cannot be introduced directly to the GNSS land applications. This is because a high data redundancy exists in the aviation domain and the hypothesis that only one failure occurs at a time is made, which is not the case for the urban users. The main objective of this paper is to provide an overview of the past and current literature discussing the GNSS integrity for urban transport applications so as to point out possible challenges faced by GNSS receivers in such scenario. Key differences between integrity monitoring scheme in aviation domain and urban transport field are addressed. And this paper also points out several open research issues in this field.

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GNSS Position Integrity in Urban Environments: A Review of Literature

Cooperative localization of the connected vehicles is significant for many advanced intelligent transportation system (ITS) applications. Vehicle-to-vehicle communication using dedicated short-range communication (DSRC) has great potential to enhance global navigation satellite systems (GNSSs) for the capability of cooperative localization. In the integration of DSRC and GNSS, the tolerance against the unknown and time-varying observation conditions is a key factor to fulfill the requirements of several specific ITS applications. Under a GNSS/DSRC integrated architecture for cooperative localization, a novel robust cubature Kalman filter (CKF) is proposed in this paper to improve the performance of the data fusion under uncertain sensor observation environments. In the proposed solution, the structure of the standard CKF is enhanced using the Huber M-estimation technique, in which the original measurement update in the CKF is modified considering the probable anomalies in state estimation. Furthermore, based on the investigation of the adjustment effect from the constraint factor, an adaptive strategy for this parameter is introduced to optimize the performance comprehensively. The proposed method is validated using a specific simulation system. Results of experiment and simulations demonstrate the capability of improving the robustness and adaptive performance over the original filters under the unknown operation conditions.

Cooperative Localization of Connected Vehicles: Integrating GNSS With DSRC Using a Robust Cubature Kalman Filter

Global Navigation Satellite Systems (GNSS) are widely spread (with Global Positioning System - GPS) in intelligent transport systems and offer a low cost, continuous and global solution for positioning. Unfortunately, urban users are often the most demanding of accurate localization but receive a degraded service because of signal propagation conditions. Several mitigation solutions can be developed. We propose, within CAPLOC project (2010-2013) to deal with inaccuracy by associating image processing techniques and signal propagation knowledge. In this paper, we focus on the contribution of image processing in more accurate position estimation. Thus, we use a laboratory vehicle, which is equipped with a fisheye camera and two GNSS receivers. The camera is located on the roof and oriented upwards to capture images of the sky. The GNSS receivers are used to obtain raw data, the position of the vehicle and the reference trajectory. The proposed approach consists in determining where satellites are located in the fisheye image, and then excluding those located in non-sky regions when calculating the position. For that, the strategy is based on an image simplification step coupled with a pixels classification. The image-based exclusion procedure is compared with the classical one based on the application of a threshold on carrier-to-noise (CN0) ratio to separate LOS and NLOS signals. Accuracy improvement is satisfying with the CN0-based method and show an improvement from 13m to 4,5m. Image-based detection shows mixed improvements but promising: good in a static area and too harsh in another configuration of the scenario.

/pdf/toward-accurate-localization-in-guided-transport-combining-1nxyn8056p.pdf

Toward accurate localization in guided transport: combining GNSS data and imaging information

This paper investigates the problem of accuracy of localization with GNSS in constraint environments. The ultimate goal is to provide a first confidence index on the accuracy of the position given by the GNSS. In this paper, we propose to use the complementarity between the GNSS signals and the development in image processing to count satellites with direct reception state. It consists to use a vehicle equipped with a GPS-RTK and a camera oriented upwards to capture images and count after repositioning, the satellites with direct signals (resp. with blocked/reflected signals) i.e. located in the sky region of the image (resp. located in the not-sky region). The proposed approach is based on an optimal clustering applied on simplified images. More preciously, the acquired image is simplified using a geodesic reconstruction with an optimal contrast parameter. Then, a clustering step is made in order to classify the regions into two classes (sky and not-sky). For that, a set of unsupervised (KMlocal, Fuzzy C-means, Fisher and Statistical region Merging) and supervised (Bayes, K-Nearest Neighbor and Support Vector Machine) clustering algorithms are compared in order to define the best classifier in terms of good classification rate and processing time. Experimental results are shown for hundred images taken in different conditions of acquisition (illumination changes, clouds, sun, tunnels, etc).

Counting of satellites with direct GNSS signals using Fisheye camera: A comparison of clustering algorithms

This paper is focused on real time detection of satellite reception state In constrained environment, such as urban areas, GNSS signals can be received directly, reflected or blocked by obstacles (building, vegetation, etc) and can lead to an error or a lack of positioning This paper proposes to characterize the GNSS signals reception environment using image analysis The proposed approach consists of detecting the visible sky part in the images thanks to a k-means based classification algorithm applied in segmented/simplified images In this paper, two images segmentation/simplification techniques are proposed and compared in terms of robustness, reliability and processing rate The first one is based on a combination of color and texture information The second technique uses a geodesic reconstruction by dilatation with an optimal contrast parameter The main aim of this work consists of determining how many satellites are positioning in the area of visible sky and have a direct reception state Experimental classification and positioning results, using real data and an evaluation methodology are presented to demonstrate the effectiveness and the reliability of the proposed approach

Image analysis based real time detection of satellites reception state

In this paper, we present a new combination of colour and texture informations for image segmentation. This technique is based on principal components analysis of a 3D points cloud, followed by an eigenvalues analysis. A set of colour gradients (morphological, Di-Zenzo) and texture gradients (Gabor, three Haralick attributes, Alternative Sequential Filter (ASF)) are used to test the proposed combination. The segmentation is performed using a hybrid gradient based watershed algorithm. The major contribution of this work consists in combining locally colour and texture informations using an adaptive and non parametric approach. The proposed method is tested on 100 images from the Berkley dataset [1] and evaluated with the Mean Square Error (MSE), the Variation of Information (VI) and the Probabilistic Rand Index (PRI).

/pdf/eigen-combination-of-colour-and-texture-informations-for-265gnab1is.pdf

D. Attia

Papers

Toward accurate localization in guided transport: combining GNSS data and imaging information

Counting of satellites with direct GNSS signals using Fisheye camera: A comparison of clustering algorithms

Image analysis based real time detection of satellites reception state

Eigen combination of colour and texture informations for image segmentation