Efficient Constellation-Based Map-Merging for Semantic SLAM

Data association in SLAM is fundamentally challenging, and handling ambiguity well is crucial to achieve robust operation in real-world environments. When ambiguous measurements arise, conservatism often mandates that the measurement is discarded or a new landmark is initialized rather than risking an incorrect association. To address the inevitable `duplicate' landmarks that arise, we present an efficient map-merging framework to detect duplicate constellations of landmarks, providing a high-confidence loop-closure mechanism well-suited for object-level SLAM. This approach uses an incrementally-computable approximation of landmark uncertainty that only depends on local information in the SLAM graph, avoiding expensive recovery of the full system covariance matrix. This enables a search based on geometric consistency (GC) (rather than full joint compatibility (JC)) that inexpensively reduces the search space to a handful of `best' hypotheses. Furthermore, we reformulate the commonly-used interpretation tree to allow for more efficient integration of clique-based pairwise compatibility, accelerating the branch-and-bound max-cardinality search. Our method is demonstrated to match the performance of full JC methods at significantly-reduced computational cost, facilitating robust object-based loop-closure over large SLAM problems.Comment: Accepted to IEEE International Conference on Robotics and Automation (ICRA) 201

Um algoritmo exato para o problema de realocação de blocos usando novos limitantes inferiores

Orientadores: Eduardo Candido Xavier, Carla Negri LintzmayerDissertação (mestrado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: O Problema de Realocação de Blocos é um problema importante em sistemas de armazenamento. Um exemplo de entrada para este problema consiste em um conjunto de blocos distribuídos em pilhas, onde cada bloco é identicado por um número que representa sua prioridade de recuperação e todas as pilhas têm um mesmo limite de altura. Apenas blocos no topo de uma pilha podem ser movidos, com dois tipos de movimentos: ou um bloco é recuperado, o que ocorre quando ele tem a mais alta prioridade de recuperação entre os blocos empilhados, ou um bloco é realocado do topo de uma pilha para o topo de outra pilha. O objetivo é recuperar todos os blocos, respeitando sua prioridade de recuperação e executando o menor número de realocações. Resolver este problema é crítico em sistemas de armazenamento, pois economiza tempo e recursos operacionais. Apresentamos dois novos limitantes inferiores para o número de realocações em uma solução ótima. Implementamos um algoritmo de deepening A* usando esses limites inferiores propostos e outros limites inferiores bem conhecidos da literatura. Foi realizado um extenso conjunto de experimentos computacionais mostrando que os novos limites inferiores melhoram o desempenho do algoritmo exato, resolvendo mais instâncias otimamente do que quando usando outros limites inferiores na mesma quantidade de tempoAbstract: The Blocks Relocation Problem is an important problem in storage systems. An input instance for this problem consists of a set of blocks distributed in stacks where each block is identified by a retrieval priority number and each stack has the same maximum height limit. Only blocks at the top of a stack can be moved: either a block is retrieved, if it has the highest retrieval priority among the stacked blocks, or it is relocated to the top of another stack. The objective is to retrieve all the blocks, respecting their retrieval priority while performing the minimum number of relocations. Solving this problem is critical in storage systems because it saves operational time and resources. We present two new lower bounds for the number of relocations of an optimal solution. We implemented an iterative deepening A* algorithm using these new proposed lower bounds and other well- known lower bounds from the literature. We performed an extensive set of computational experiments showing that the new lower bounds improve the performance of the exact algorithm, solving to optimality more instances than when using other lower bounds in the same amount of timeMestradoCiência da ComputaçãoMestre em Ciência da ComputaçãoCAPE

Integer programming models for the pre-marshalling problem

[EN] The performance of shipping companies greatly depends on reduced berthing times. The trend towards bigger ships and shorter berthing times places severe stress on container terminals, which cannot simply increase the available cranes indefinitely. Therefore, the focus is on optimizing existing resources. An effective way of speeding up the loading/unloading operations of ships at the container terminal is to use the idle time before the arrival of a ship for sorting the stored containers in advance. The pre-marshalling problem consists in rearranging the containers placed in a bay in the order in which they will be required later, looking for a sequence with the minimum number of moves. With sorted bays, loading/unloading operations are significantly faster, as there is no longer a need to make unproductive moves in the bays once ships are berthed. In this paper, we address the pre-marshalling problem by developing and testing integer linear programming models. Two alternative families of models are proposed, as well as an iterative solution procedure that does not depend on a difficult to obtain upper bound. An extensive computational analysis has been carried out over several well-known datasets from the literature. This analysis has allowed us to test the performance of the models, and to conclude that the performance of the best proposed model is superior to that of previously published alternatives.This study has been partially supported by the Spanish Ministry of Education, Culture, and Sport, FPU Grant A-2015-12849 and by the Spanish Ministry of Economy and Competitiveness, under projects DPI2014-53665-P and DPI2015-65895-R, partially financed with FEDER funds.Parreño-Torres, C.; Alvarez-Valdes, R.; Ruiz García, R. (2019). Integer programming models for the pre-marshalling problem. European Journal of Operational Research. 274(1):142-154. https://doi.org/10.1016/j.ejor.2018.09.048S142154274

Column Generation for the Container Relocation Problem

Container terminals offer transfer facilities to move containers from vessels to trucks, trains and barges and vice versa. Within the terminal the container yard serves as a temporary buffer where incoming containers are piled up in stacks. Only the topmost container of each stack can be accessed. If another container has to be retrieved, containers stored above it must be relocated first. Containers need to be transported to a ship or to trucks in a predefined sequence as fast as possible. Generally, this sequence does not match the stacking order within the yard. Therefore, a sequence of retrieval and relocation movements has to be determined that retrieves containers from the bay in the prescribed order with a minimum number of relocations. This problem is known as the container relocation problem. We apply an exact and a heuristic column generation approach to this problem. First results are very promising since both approaches provide very tight lower bounds on the minimum number of relocations

Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem

In container terminals, containers are often moved to other stacks in order to access containers that need to leave the terminal earlier. We propose a new optimization model in which the containers can be moved in two different phases: a pre-processing and a relocation phase. To solve this problem, we develop an optimal branch-and-bound algorithm. Furthermore, we develop a local search heuristic because the problem is NP-hard. Besides that, we give a rule-based method to estimate the number of relocation moves in a bay. The local search heuristic produces solutions that are close to the optimal solution. Finally, for instances in which the benefits of moving containers in the two different phases are in balance, the solution of the heuristic yields significant improvement compared to the existing methods in which containers are only moved in one of the two phases