Author name cluster

Ioan Andrei Bârsan

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4 papers

1 author row

ICRA Conference 2021 Conference Paper

Asynchronous Multi-View SLAM

Anqi Joyce Yang
Can Cui
Ioan Andrei Bârsan
Raquel Urtasun
Shenlong Wang

Existing multi-camera SLAM systems assume synchronized shutters for all cameras, which is often not the case in practice. In this work, we propose a generalized multi-camera SLAM formulation which accounts for asynchronous sensor observations. Our framework integrates a continuous-time motion model to relate information across asynchronous multi-frames during tracking, local mapping, and loop closing. For evaluation, we collected AMV-Bench, a challenging new SLAM dataset covering 482 km of driving recorded using our asynchronous multi-camera robotic platform. AMV-Bench is over an order of magnitude larger than previous multi-view HD outdoor SLAM datasets, and covers diverse and challenging motions and environments. Our experiments emphasize the necessity of asynchronous sensor modeling, and show that the use of multiple cameras is critical towards robust and accurate SLAM in challenging outdoor scenes. The supplementary material is located at: https://www.cs.toronto.edu/~ajyang/amv-slam

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IROS Conference 2020 Conference Paper

Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars

Julieta Martinez 0001
Sasha Doubov
Jack Fan
Ioan Andrei Bârsan
Shenlong Wang
Gellért Máttyus
Raquel Urtasun

We are interested in understanding whether retrieval-based localization approaches are good enough in the context of self-driving vehicles. Towards this goal, we introduce Pit30M, a new image and LiDAR dataset with over 30 million frames, which is 10 to 100 times larger than those used in previous work. Pit30M is captured under diverse conditions (i. e. , season, weather, time of the day, traffic), and provides accurate localization ground truth. We also automatically annotate our dataset with historical weather and astronomical data, as well as with image and LiDAR semantic segmentation as a proxy measure for occlusion. We benchmark multiple existing methods for image and LiDAR retrieval and, in the process, introduce a simple, yet effective convolutional network-based LiDAR retrieval method that is competitive with the state of the art. Our work provides, for the first time, a benchmark for sub-metre retrieval-based localization at city scale. The dataset, additional experimental results, as well as more information about the sensors, calibration, and metadata, are available on the project website: https://uber.com/atg/datasets/pit30m.

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IROS Conference 2019 Conference Paper

Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization

Wei-Chiu Ma
Raquel Urtasun
Ignacio Tartavull
Ioan Andrei Bârsan
Shenlong Wang
Min Bai
Gellért Máttyus
Namdar Homayounfar

In this paper we propose a novel semantic localization algorithm that exploits multiple sensors and has precision on the order of a few centimeters. Our approach does not require detailed knowledge about the appearance of the world, and our maps require orders of magnitude less storage than maps utilized by traditional geometry- and LiDAR intensity-based localizers. This is important as self-driving cars need to operate in large environments. Towards this goal, we formulate the problem in a Bayesian filtering framework, and exploit lanes, traffic signs, as well as vehicle dynamics to localize robustly with respect to a sparse semantic map. We validate the effectiveness of our method on a new highway dataset consisting of 312km of roads. Our experiments show that the proposed approach is able to achieve 0. 05m lateral accuracy and 1. 12m longitudinal accuracy on average while taking up only 0. 3% of the storage required by previous LiDAR intensity-based approaches.

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ICRA Conference 2018 Conference Paper

Robust Dense Mapping for Large-Scale Dynamic Environments

Ioan Andrei Bârsan
Peidong Liu 0001
Marc Pollefeys
Andreas Geiger 0001

We present a stereo-based dense mapping algorithm for large-scale dynamic urban environments. In contrast to other existing methods, we simultaneously reconstruct the static background, the moving objects, and the potentially moving but currently stationary objects separately, which is desirable for high-level mobile robotic tasks such as path planning in crowded environments. We use both instance-aware semantic segmentation and sparse scene flow to classify objects as either background, moving, or potentially moving, thereby ensuring that the system is able to model objects with the potential to transition from static to dynamic, such as parked cars. Given camera poses estimated from visual odometry, both the background and the (potentially) moving objects are reconstructed separately by fusing the depth maps computed from the stereo input. In addition to visual odometry, sparse scene flow is also used to estimate the 3D motions of the detected moving objects, in order to reconstruct them accurately. A map pruning technique is further developed to improve reconstruction accuracy and reduce memory consumption, leading to increased scalability. We evaluate our system thoroughly on the well-known KITTI dataset. Our system is capable of running on a PC at approximately 2. 5Hz, with the primary bottleneck being the instance-aware semantic segmentation, which is a limitation we hope to address in future work. The source code is available from the project website a a http://andreibarsan.github.io/dynslam.

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