This paper presents a new reflectance model for rendering computer synthesized images. The model accounts for the relative brightness of different materials and light sources in the same scene. It describes the directional distribution of the reflected light and a color shift that occurs as the reflectance changes with incidence angle. The paper presents a method for obtaining the spectral energy distribution of the light reflected from an object made of a specific real material and discusses a procedure for accurately reproducing the color associated with the spectral energy distribution. The model is applied to the simulation of a metal and a plastic.

A reflectance model for computer graphics

Learning and then recognizing a route, whether travelled during the day or at night, in clear or inclement weather, and in summer or winter is a challenging task for state of the art algorithms in computer vision and robotics. In this paper, we present a new approach to visual navigation under changing conditions dubbed SeqSLAM. Instead of calculating the single location most likely given a current image, our approach calculates the best candidate matching location within every local navigation sequence. Localization is then achieved by recognizing coherent sequences of these “local best matches”. This approach removes the need for global matching performance by the vision front-end - instead it must only pick the best match within any short sequence of images. The approach is applicable over environment changes that render traditional feature-based techniques ineffective. Using two car-mounted camera datasets we demonstrate the effectiveness of the algorithm and compare it to one of the most successful feature-based SLAM algorithms, FAB-MAP. The perceptual change in the datasets is extreme; repeated traverses through environments during the day and then in the middle of the night, at times separated by months or years and in opposite seasons, and in clear weather and extremely heavy rain. While the feature-based method fails, the sequence-based algorithm is able to match trajectory segments at 100% precision with recall rates of up to 60%.

SeqSLAM : visual route-based navigation for sunny summer days and stormy winter nights

In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM) field when considering the recent evolution of autonomous driving. The growing interest regarding self-driving cars has given new directions to localization and mapping techniques. In this survey, we give an overview of the different branches of SLAM before going into the details of specific trends that are of interest when considered with autonomous applications in mind. We first present the limits of classical approaches for autonomous driving and discuss the criteria that are essential for this kind of application. We then review the methods where the identified challenges are tackled. We mostly focus on approaches building and reusing long-term maps in various conditions (weather, season, etc.). We also go through the emerging domain of multivehicle SLAM and its link with self-driving cars. We survey the different paradigms of that field (centralized and distributed) and the existing solutions. Finally, we conclude by giving an overview of the various large-scale experiments that have been carried out until now and discuss the remaining challenges and future orientations.

/pdf/simultaneous-localization-and-mapping-a-survey-of-current-4t1s8zrzpj.pdf

Simultaneous Localization and Mapping: A Survey of Current Trends in Autonomous Driving

Autonomous vehicles (AV) are expected to improve, reshape, and revolutionize the future of ground transportation. It is anticipated that ordinary vehicles will one day be replaced with smart vehicles that are able to make decisions and perform driving tasks on their own. In order to achieve this objective, self-driving vehicles are equipped with sensors that are used to sense and perceive both their surroundings and the faraway environment, using further advances in communication technologies, such as 5G. In the meantime, local perception, as with human beings, will continue to be an effective means for controlling the vehicle at short range. In the other hand, extended perception allows for anticipation of distant events and produces smarter behavior to guide the vehicle to its destination while respecting a set of criteria (safety, energy management, traffic optimization, comfort). In spite of the remarkable advancements of sensor technologies in terms of their effectiveness and applicability for AV systems in recent years, sensors can still fail because of noise, ambient conditions, or manufacturing defects, among other factors; hence, it is not advisable to rely on a single sensor for any of the autonomous driving tasks. The practical solution is to incorporate multiple competitive and complementary sensors that work synergistically to overcome their individual shortcomings. This article provides a comprehensive review of the state-of-the-art methods utilized to improve the performance of AV systems in short-range or local vehicle environments. Specifically, it focuses on recent studies that use deep learning sensor fusion algorithms for perception, localization, and mapping. The article concludes by highlighting some of the current trends and possible future research directions.

https://hal.archives-ouvertes.fr/hal-02942600/document

Deep Learning Sensor Fusion for Autonomous Vehicle Perception and Localization: A Review.

The prediction of lane changes has been proven to be useful for collision avoidance support in road vehicles. This paper proposes an interactive multiple model (IMM)-based method for predicting lane changes in highways. The sensor unit consists of a set of low-cost Global Positioning System/inertial measurement unit (GPS/IMU) sensors and an odometry captor for collecting velocity measurements. Extended Kalman filters (EKFs) running in parallel and integrated by an IMM-based algorithm provide positioning and maneuver predictions to the user. The maneuver states Change Lane (CL) and Keep Lane (KL) are defined by two models that describe different dynamics. Different model sets have been studied to meet the needs of the IMM-based algorithm. Real trials in highway scenarios show the capability of the system to predict lane changes in straight and curved road stretches with very short latency times.

/pdf/imm-based-lane-change-prediction-in-highways-with-low-cost-2zm2w0z6u7.pdf

IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS

Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

Clinical evaluation of frailty in the elderly is the first step to decide the degree of assistance they require. Advances in robotics make it possible to turn a standard assistance device into an augmented device that may enrich the existing tests with new sets of daily measured criteria. In this paper we use a standard 4 wheeled rollator, equipped with a Kinect and odometers, for biomechanical gait analysis. This paper focuses on the method we develop to measure and estimate legs and feet position during an assisted walk. The results are compared with motion capture data, as a ground truth. Preliminary results obtained on four healthy persons show that relevant data can be extracted for gait analysis. Some criteria are accurate with regards to the ground truth, eg. foot orientation and ankle angle.

https://hal.inria.fr/hal-00921273/document

Feet and legs tracking using a smart rollator equipped with a Kinect

This paper introduces a topological localization algorithm that uses visual and Wi-Fi data. Its main contribution is a novel way of merging data from these sensors. By making Wi-Fi signature suited to FABMAP algorithm, it develops an early-fusion framework that solves global localization and kidnapped robot problem. The resulting algorithm is tested and compared to FABMAP visual localization, over data acquired by a Pepper robot in an office building. Several constraints were applied during acquisition to make the experiment fitted to real-life scenarios. Without any tuning, early-fusion surpasses the performances of visual localization by a significant margin: 94% of estimated localizations are less than 5m away from ground truth compared to 81% with visual localization.

https://hal-mines-paristech.archives-ouvertes.fr/hal-01691920/document

Topological localization using Wi-Fi and vision merged into FABMAP framework

This paper presents a metric global localization in the urban environment only with a monocular camera and the Google Street View database. We fully leverage the abundant sources from the Street View and benefits from its topo-metric structure to build a coarse-to-fine positioning, namely a topological place recognition process and then a metric pose estimation by local bundle adjustment. Our method is tested on a 3 km urban environment and demonstrates both sub-meter accuracy and robustness to viewpoint changes, illumination and occlusion. To our knowledge, this is the first work that studies the global urban localization simply with a single camera and Street View.

Monocular urban localization using street view

INRIA Sophia Antipolis M´editerran´ee, 2004 route des Lucioles, Sophia Antipolis Cedex, Francecyril.joly@sophia.inria.fr, patrick.rives@sophia.inria.frKeywords: Simultaneous Localization and Mapping (SLAM), Smoothing and Mapping (SAM), ExtendedKalman Filter (EKF), Bearing-Only, Inverse Depth RepresentationAbstract: Safe and eﬃcient navigation in large-scale unknown environments remains a key problem whichhas to be solved to improve the autonomy of mobile robots. SLAM methods can bring the mapof the world and the trajectory of the robot. Monucular SLAM is a diﬃcult problem. Currently,it is solved with an Extended Kalman Filter (EKF) using the inverse depth parametrization.However, it is now well known that the EKF-SLAM become inconsistent when dealing with largescale environments. Moreover, the classical inverse depth parametrization is over-parametrized,which can also be a cause of inconsistency. In this paper, we propose to adapt the inverse depthrepresentation to the more robust context of smoothing and mapping (SAM). We show that ouralgorithm is not over-parametrized and that it gives very precise results on real data.

Cyril Joly

Papers

Seamless navigation and mapping using an INS/GNSS/grid-based SLAM semi-tightly coupled integration scheme

Feet and legs tracking using a smart rollator equipped with a Kinect

Topological localization using Wi-Fi and vision merged into FABMAP framework

Monocular urban localization using street view

BEARING-ONLY SAM USING A MINIMAL INVERSE DEPTH PARAMETRIZATION - Application to Omnidirectional SLAM