CNN for IMU assisted odometry estimation using velodyne LiDAR

Veľas, Martin; Španěl, Michal; Hradiš, Michal; Herout, Adam

doi:10.1109/icarsc.2018.8374163

Cited by 79 publications

(100 citation statements)

References 18 publications

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“…M. Velas et al [32] encodes the 3D LiDAR data into a specific 2D representation designed for multi-beam mechanical LiDARs. CNNs is used to infer the 6 DOF poses as a classification or regression problem.…”

Section: Related Workmentioning

confidence: 99%

DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration

Wan

Zhou

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

270

168

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We present DeepICP -a novel end-to-end learningbased 3D point cloud registration framework that achieves comparable registration accuracy to prior state-of-the-art geometric methods. Different from other keypoint based methods where a RANSAC procedure is usually needed, we implement the use of various deep neural network structures to establish an end-to-end trainable network. Our keypoint detector is trained through this end-to-end structure and enables the system to avoid the inference of dynamic objects, leverages the help of sufficiently salient features on stationary objects, and as a result, achieves high robustness. Rather than searching the corresponding points among existing points, the key contribution is that we innovatively generate them based on learned matching probabilities among a group of candidates, which can boost the registration accuracy. Our loss function incorporates both the local similarity and the global geometric constraints to ensure all above network designs can converge towards the right direction. We comprehensively validate the effectiveness of our approach using both the KITTI dataset and the Apollo-SouthBay dataset. Results demonstrate that our method achieves comparable or better performance than the state-of-the-art geometry-based methods. Detailed ablation and visualization analysis are included to further illustrate the behavior and insights of our network. The low registration error and high robustness of our method makes it attractive for substantial applications relying on the point cloud registration task.

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Section: Related Workmentioning

confidence: 99%

DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration

Wan

Zhou

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

270

168

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“…Baselines. We compare our approach with several existing lidar odometry estimation methods: ICP-point2point (ICP-po2po), ICP-point2plane (ICP-po2pl), GICP (Segal et al, 2009), CLS (Velas et al, 2016) and Velas et al (Velas et al, 2018). The first two ICP methods are implemented using the Point Cloud Library (Rusu , Cousins, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…The first two ICP methods are implemented using the Point Cloud Library (Rusu , Cousins, 2011). As far as we know, (Velas et al, 2018) is the only deep learning based lidar odometry method that has comparable results. Loop closure detection is not implemented for all methods since we aim to test the limits of accurate odometry estimation.…”

Section: Methodsmentioning

confidence: 99%

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Deep Lidar Odometry

Wang

Chen

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Most existing lidar odometry estimation strategies are formulated under a standard framework that includes feature selection, and pose estimation through feature matching. In this work, we present a novel pipeline called LO-Net for lidar odometry estimation from 3D lidar scanning data using deep convolutional networks. The network is trained in an end-to-end manner, it infers 6-DoF poses from the encoded sequential lidar data. Based on the new designed mask-weighted geometric constraint loss, the network automatically learns effective feature representation for the lidar odometry estimation problem, and implicitly exploits the sequential dependencies and dynamics. Experiments on benchmark datasets demonstrate that LO-Net has similar accuracy with the geometry-based approach.

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“…They presented an interesting approach that provided a reasonable estimation of odometry, however still not competitive with the efficiency of state-of-the-art scan matching methods. Later, Velas et al [23] presented another approach for using CNNs with 3D laser scanners for IMU assisted odometry. Their results were able to get high precision and close results compared to state-of-the-art methods, such as LOAM [18], for translation, however the method is not able to estimate rotation with sufficient precision.…”

Section: Related Workmentioning

confidence: 99%

An LSTM Network for Real-Time Odometry Estimation

Valente

Joly

Fortelle

2019

2019 IEEE Intelligent Vehicles Symposium (IV)

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The use of 2D laser scanners is attractive for the autonomous driving industry because of its accuracy, light-weight and low-cost. However, since only a 2D slice of the surrounding environment is detected at each scan, it is a challenge to execute important tasks such as the localization of the vehicle. In this paper we present a novel framework that explores the use of deep Recurrent Convolutional Neural Networks (RCNN) for odometry estimation using only 2D laser scanners. The application of RCNNs provides the tools to not only extract the features of the laser scanner data using Convolutional Neural Networks (CNNs), but in addition it models the possible connections among consecutive scans using the Long Short-Term Memory (LSTM) Recurrent Neural Network. Results on a real road dataset show that the method can run in real-time without using GPU acceleration and have competitive performance compared to other methods, being an interesting approach that could complement traditional localization systems.

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CNN for IMU assisted odometry estimation using velodyne LiDAR

Cited by 79 publications

References 18 publications

DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration

DeepVCP: An End-to-End Deep Neural Network for Point Cloud Registration

Deep Lidar Odometry

An LSTM Network for Real-Time Odometry Estimation

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