Improving SLAM with Drift Integration

International audienceLocalization without prior knowledge can be a difficult task for a vehicle. An answer to this problematic lies in the Simultaneous Localization And Mapping (SLAM) approach where a map of the surroundings is built while simultaneously being used for localization purposes. However, SLAM algorithms tend to drift over time, making the localization inconsistent. In this paper, we propose to model the drift as a localization bias and to integrate it in a general architecture. The latter allows any feature-based SLAM algorithm to be used while taking advantage of the drift integration. Based on previous works, we extend the bias concept and propose a new architecture which drastically improves the performance of our method, both in terms of computational power and memory required. We validate this framework on real data with different scenarios. We show that taking into account the drift allows us to maintain consistency and improve the localization accuracy with almost no additional cost

Real-Time Monocular SLAM With Low Memory Requirements

International audienceThe localization of a vehicle in an unknown environment is often solved using Simultaneous Localization And Mapping (SLAM) techniques. Many methods have been developed , each requiring a different amount of landmarks (map size) , and so of memory , to work efficiently. Similarly , the required computational time is quite variable from one approach to another. In this paper , we focus on the monocular SLAM problem and propose a new method , called MSLAM , based on an Extended Kalman Filter (EKF). The aim is to provide a solution that has low memory and processing time requirements and that can achieve good localization results while benefiting from the EKF advantages (direct access to the covariance matrix , no conversion required for the measures or the state). To do so , a minimal Cartesian representation (3 parameters for 3 dimensions) is used. However , linearization errors are likely to happen with such a representation. New methods allowing to avoid or hugely decrease the impact of the linearization failures are presented. The first contribution proposed here computes a proper projection of a 3D uncertainty in the image plane , allowing to track landmarks during longer periods of time. A corrective factor of the Kalman gain is also introduced. It allows to detect wrong updates and correct them , thus reducing the impact of the linearization on the whole system. Our approach is compared to a classic SLAM implementation over different data sets and conditions so as to illustrate the efficiency of the proposed contributions. The quality of the map built is tested by using it with another vehicle for localization purposes. Finally , a public data set , presenting a long trajectory (1. 3 km) is also used in order to compare MSLAM to a state-of-the-art monocular EKF-SLAM algorithm , both in terms of accuracy and computational needs

Finding Maximum Cliques on a Quantum Annealer

This paper assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance. For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, and compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics. We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW. We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms. On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches

Climate change, animal product consumption and the future of food systems

The livestock sector contributes around 14.5 percent of total anthropogenic greenhouse gas (GHG) emissions. Developing mitigation strategies is a serious challenge, especially if we anticipate a rapid growth in the consumption of animal products in Low-Income (LI) and Lower Middle-Income (LMI) countries. Across the planet, livestock systems are highly diverse and the livestock sector offers many possibilities for carbon sinking that can help to reduce emissions. In particular, carbon sequestration in grasslands, rangelands and feed crop fields and manure recycling are crucial in the assessment of the carbon efficiency of livestock value chains. Supporting sustainable livestock production systems, together with sustainable animal product market chains and consumption, requires the completion of GHG inventories based on landscape carbon balances

Designing Decentralized controllers for distributed-air-jet MEMS-based micromanipulators by reinforcement learning.

International audienceDistributed-air-jet MEMS-based systems have been proposed to manipulate small parts with high velocities and without any friction problems. The control of such distributed systems is very challenging and usual approaches for contact arrayed system don't produce satisfactory results. In this paper, we investigate reinforcement learning control approaches in order to position and convey an object. Reinforcement learning is a popular approach to find controllers that are tailored exactly to the system without any prior model. We show how to apply reinforcement learning in a decentralized perspective and in order to address the global-local trade-off. The simulation results demonstrate that the reinforcement learning method is a promising way to design control laws for such distributed systems

Parallel seed-based approach to multiple protein structure similarities detection

Finding similarities between protein structures is a crucial task in molecular biology. Most of the existing tools require proteins to be aligned in order-preserving way and only find single alignments even when multiple similar regions exist. We propose a new seed-based ap-proach that discovers multiple pairs of similar regions. Its computa-tional complexity is polynomial and it comes with a quality guarantee– the returned alignments have both Root Mean Squared Deviations (coordinate-based as well as internal-distances based) lower than a given threshold, if such exist. We do not require the alignments to be order preserving (i.e. we consider non-sequential alignments), which makes our algorithm suitable for detecting similar domains when com-paring multi-domain proteins as well as to detect structural repetitions within a single protein. Because the search space for non-sequential alignments is much larger than for sequential ones, the computational burden is addressed by extensive use of parallel computing techniques: a coarse-grain level parallelism making use of available CPU cores for computation and a fine-grain level parallelism exploiting bit-level con-currency as well as vector instructions

Major floods of the V\'esubie and Roya Rivers (Alps, France) in October 2020: hydrogeomorphological caracterisation and management perspectives

On October 2nd, 2020, under the combined effect of the winter Alex storm formed off the Brittany coast, and a strong Mediterranean episode, very intensive rainfalls affected in the south eastern France, both Roya and V{\'e}subie catchments (locally up to 600 mm in 24h). This paroxysmal event with a heavy human toll (10 dead, 8 missing) generated extreme flash floods over a large part of the hydrographic network. The result is an almost generalized fluvial metamorphosis of rivers, from sinuous single-thread channels to braided channels. The characterization of morphological effects of these floods is based on a diachronic aerial picture analysis highlighting a strong increase of the active channel width (up to 900%) reaching -- or even pushing back in few sectors -- front limits of the valley bottom. In the V{\'e}subie, the 2D morphological effect of the Alex storm was 10 times higher than that of the 100-yrs return period flood of November 1997. Comparison of digital terrain models (DEM) before- and after-flood also allows us to foresee the altitudinal variations (erosion/deposition) that affected beds and their riverine margins. The analysis of the impacts caused by these floods changes the perception of the ``freedom space'' of these alpine rivers, which now must be taken into account in the perspective of resilient reconstruction.Comment: in French language. IS Rivers 2022, ZABR; GRAIE, Jul 2022, Lyon, Franc