Titus Cieslewski

The gist of maps - summarizing experience for lifelong localization

Robust, scalable place recognition is a core competency for many robotic applications. However, when revisiting places over and over, many state-of-the-art approaches exhibit reduced performance in terms of computation and memory complexity and in terms of accuracy. For successful deployment of robots over long time scales, we must develop algorithms that get better with repeated visits to the same environment, while still working within a fixed computational budget. This paper presents and evaluates an algorithm that alternates between online place recognition and offline map maintenance with the goal of producing the best performance with a fixed map size. At the core of the algorithm is the concept of a Summary Map, a reduced map representation that includes only the landmarks that are deemed most useful for place recognition. To assign landmarks to the map, we use a scoring function that ranks the utility of each landmark and a sampling policy that selects the landmarks for each place. The Summary Map can then be used by any descriptor-based inference method for constant-complexity online place recognition. We evaluate a number of scoring functions and sampling policies and show that it is possible to build and maintain maps of a constant size and that place-recognition performance improves over multiple visits.

Point cloud descriptors for place recognition using sparse visual information

Place recognition is a core component in simultaneous localization and mapping (SLAM), limiting positional drift over space and time to unlock precise robot navigation. Determining which previously visited places belong together continues to be a highly active area of research as robotic applications demand increasingly higher accuracies. A large number of place recognition algorithms have been proposed, capable of consuming a variety of sensor data including laser, sonar and depth readings. The best performing solutions, however, have utilized visual information by either matching entire images or parts thereof. Most commonly, vision based approaches are inspired by information retrieval and utilize 3D-geometry information about the observed scene as a post-verification step. In this paper we propose to use the 3D-scene information from sparse-visual feature maps directly at the core of the place recognition pipeline. We propose a novel structural descriptor which aggregates sparse triangulated landmarks from SLAM into a compact signature. The resulting 3D-features provide a discriminative fingerprint to recognize places over seasonal and viewpoint changes which are particularly challenging for approaches based on sparse visual descriptors. We evaluate our system on publicly available datasets and show how its complementary nature can provide an improvement over visual place recognition.

Dynamic collaboration without communication: Vision-based cable-suspended load transport with two quadrotors

Transport of objects is a major application in robotics nowadays. While ground robots can carry heavy payloads for long distances, they are limited in rugged terrains. Aerial robots can deliver objects in arbitrary terrains; however they tend to be limited in payload. It has been previously shown that, for heavy payloads, it can be beneficial to carry them using multiple flying robots. In this paper, we propose a novel collaborative transport scheme, in which two quadrotors transport a cable-suspended payload at accelerations that exceed the capabilities of previous collaborative approaches, which make quasi-static assumptions. Furthermore, this is achieved completely without explicit communication between the collaborating robots, making our system robust to communication failures and making consensus on a common reference frame unnecessary. Instead, they only rely on visual and inertial cues obtained from on-board sensors. We implement and validate the proposed method on a real system.

Efficient decentralized visual place recognition from full-image descriptors

Visual multi-robot simultaneous localization and mapping (SLAM) is an effective way to provide state estimation to a group of robots that operate in an unstructured and GPS-denied environment. This is a problem that can be solved in a centralized way, but in some instances it can be desirable to solve it in a decentralized way. Decentralized visual place recognition, then, becomes a key component of a decentralized visual SLAM system. Achieving it by having all robots send queries to all other robots would use vast amounts of bandwidth, and diverse approaches have been explored by the robotics community to reduce that bandwidth. In previous work, we have proposed a decentralized version of bag-of-words place recognition, which, by using a distributed inverted index, is able to reduce bandwidth requirements by a factor of n, the robot count. In this short paper, we instead propose a decentralized visual place recognition method that is based on full-image descriptors. The method consists in clustering the full-image descriptor space into several clusters and assigning each cluster to one robot. As a result, place recognition can be achieved by sending each place query to only one robot. We evaluate the performance of our new method versus a centralized implementation using the Oxford Robotcar and KITTI datasets and explore an inherent trade-off between performance and load balancing.

Robustness to connectivity loss for collaborative mapping

Having a team of robots to perform a task such as mapping is faster and more reliable than doing the same with a single robot, which can be crucial in scenarios such as search and rescue. We are developing a fully distributed framework for collaborative mapping with large robot swarms that is robust to abrupt departure of robots due to malfunctions or network problems. While several approaches to multi-robot mapping have been proposed, most of them either build a collection of local sub-maps, or rely on a central authority to merge maps built by individual robots. Our framework is unique in that it requires no central authority, yet allows robots to simultaneously contribute to a single global map, which is stored in a decentralized fashion. This greatly improves the scalability of our system with respect to number of robots. However, our approach requires systematic coordination among robots in order to make modifications to the map. Unannounced departure of the robots makes coordination challenging, and can potentially make the map inconsistent or result in loss of data. We borrow ideas from the domain of distributed computing to address those challenges. Further, we demonstrate the robustness of the proposed system by subjecting it to various conditions in which participating robots fail.