Recently, Neural Radiance Fields (NeRF) achieved impressive results in novel view synthesis. Block-NeRF showed the capability of leveraging NeRF to build large city-scale models. For large-scale modeling, a mass of image data is necessary. Collecting images from specially designed data-collection vehicles can not support large-scale applications. How to acquire massive high-quality data remains an opening problem. Noting that the automotive industry has a huge amount of image data, crowd-sourcing is a convenient way for large-scale data collection. In this paper, we present a crowd-sourced framework, which utilizes substantial data captured by production vehicles to reconstruct the scene with the NeRF model. This approach solves the key problem of large-scale reconstruction, that is where the data comes from and how to use them. Firstly, the crowd-sourced massive data is filtered to remove redundancy and keep a balanced distribution in terms of time and space. Then a structure-from-motion module is performed to refine camera poses. Finally, images, as well as poses, are used to train the NeRF model in a certain block. We highlight that we presents a comprehensive framework that integrates multiple modules, including data selection, sparse 3D reconstruction, sequence appearance embedding, depth supervision of ground surface, and occlusion completion. The complete system is capable of effectively processing and reconstructing high-quality 3D scenes from crowd-sourced data. Extensive quantitative and qualitative experiments were conducted to validate the performance of our system. Moreover, we proposed an application, named first-view navigation, which leveraged the NeRF model to generate 3D street view and guide the driver with a synthesized video.

Sensing environmental obstacles and establishing an occupancy map of surroundings are critical to achieving automated parking for autonomous vehicles. This paper presents a method to obtain surrounding occupancy information from inverse perspective mapping (IPM) images. This method uses the easily-accessed pseudo-labels from LiDAR to supervise a visual network, which can detect occupied boundaries of obstacles. Fusing this visual occupancy with ego-motion information, we develop a multi-frame fusion approach to build a local OGM to realize online environment mapping. Compared with other learning-based occupancy approaches, our method does not require time-consuming and labor-intensive labeling for the environment due to the ground truth of surrounding occupancy coming from LiDAR easily. The proposed method achieves LiDAR-like performance with pure visual inputs, which greatly decreases the cost of real products. Experiments on driving and parking environments prove that our method can accurately sense surrounding occupancy information and build a robust occupancy map of the environment.

Combining multiple LiDARs enables a robot to maximize its perceptual awareness of environments and obtain sufficient measurements, which is promising for simultaneous localization and mapping (SLAM). This article proposes a system to achieve robust and simultaneous extrinsic calibration, odometry, and mapping for multiple LiDARs. Our approach starts with measurement preprocessing to extract edge and planar features from raw measurements. After a motion and extrinsic initialization procedure, a sliding window-based multi-LiDAR odometry runs onboard to estimate poses with an online calibration refinement and convergence identification. We further develop a mapping algorithm to construct a global map and optimize poses with sufficient features together with a method to capture and reduce data uncertainty. We validate our approach’s performance with extensive experiments on 10 sequences (4.60-km total length) for the calibration and SLAM and compare it against the state of the art. We demonstrate that the proposed work is a complete, robust, and extensible system for various multi-LiDAR setups. The source code, datasets, and demonstrations are available at:https://ram-lab.com/file/site/m-loam.

Modern LiDAR-SLAM (L-SLAM) systems have shown excellent results in large-scale, real-world scenarios. However, they commonly have a high latency due to the expensive data association and nonlinear optimization. This paper demonstrates that actively selecting a subset of features significantly improves both the accuracy and efficiency of an L-SLAM system. We formulate the feature selection as a combinatorial optimization problem under a cardinality constraint to preserve the information matrix's spectral attributes. The stochastic-greedy algorithm is applied to approximate the optimal results in real-time. To avoid ill-conditioned estimation, we also propose a general strategy to evaluate the environment's degeneracy and modify the feature number online. The proposed feature selector is integrated into a multi-LiDAR SLAM system. We validate this enhanced system with extensive experiments covering various scenarios on two sensor setups and computation platforms. We show that our approach exhibits low localization error and speedup compared to the state-of-the-art L-SLAM systems. To benefit the community, we have released the source code: https://ram-lab.com/file/site/m-loam.

In this paper, we introduce a method for visual relocalization using the geometric information from a 3D surfel map. A visual database is first built by global indices from the 3D surfel map rendering, which provides associations between image points and 3D surfels. Surfel reprojection constraints are utilized to optimize the keyframe poses and map points in the visual database. A hierarchical camera relocalization algorithm then utilizes the visual database to estimate 6-DoF camera poses. Learned descriptors are further used to improve the performance in challenging cases. We present evaluation under real-world conditions and simulation to show the effectiveness and efficiency of our method, and make the final camera poses consistently well aligned with the 3D environment.

Robustness and accuracy for monocular visual odometry (VO) under challenging environments are widely concerned. In this paper, we present a monocular VO system leveraging learned repeatability and description. In a hybrid scheme, the camera pose is initially tracked on the predicted repeatability maps in a direct manner and then refined with the patch-wise 3D-2D association. The local feature parameterization and the adapted mapping module further boost different functionalities in the system. Extensive evaluations on challenging public datasets are performed. The competitive performance on camera pose estimation demonstrates the effectiveness of our method. Additional studies on the local reconstruction accuracy and running time exhibit that our system is capable of maintaining a robust and lightweight backend.

We present LINS, a lightweight lidar-inertial state estimator, for real-time ego-motion estimation. The proposed method enables robust and efficient navigation for ground vehicles in challenging environments, such as feature-less scenes, via fusing a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. An iterated error-state Kalman filter (ESKF) is designed to correct the estimated state recursively by generating new feature correspondences in each iteration, and to keep the system computationally tractable. Moreover, we use a robocentric formulation that represents the state in a moving local frame in order to prevent filter divergence in a long run. To validate robustness and generalizability, extensive experiments are performed in various scenarios. Experimental results indicate that LINS offers comparable performance with the state-of-the-art lidar-inertial odometry in terms of stability and accuracy and has order-of-magnitude improvement in speed.

In this paper, we introduce an approach to tracking the pose of a monocular camera in a prior surfel map. By rendering vertex and normal maps from the prior surfel map, the global planar information for the sparse tracked points in the image frame is obtained. The tracked points with and without the global planar information involve both global and local constraints of frames to the system. Our approach formulates all constraints in the form of direct photometric errors within a local window of the frames. The final optimization utilizes these constraints to provide the accurate estimation of global 6-DoF camera poses with the absolute scale. The extensive simulation and real-world experiments demonstrate that our monocular method can provide accurate camera localization results under various conditions.

End-to-end behavioral cloning trained by human demonstration is now a popular approach for vision-based autonomous driving. A deep neural network maps drive-view images directly to steering commands. However, the images contain much task-irrelevant data. Humans attend to behaviorally relevant information using saccades that direct gaze towards important areas. We demonstrate that behavioral cloning also benefits from active control of gaze. We trained a conditional generative adversarial network (GAN) that accurately predicts human gaze maps while driving in both familiar and unseen environments. We incorporated the predicted gaze maps into end-to-end networks for two behaviors: following and overtaking. Incorporating gaze information significantly improves generalization to unseen environments. We hypothesize that incorporating gaze information enables the network to focus on task critical objects, which vary little between environments, and ignore irrelevant elements in the background, which vary greatly.

Ego-motion estimation is a fundamental requirement for most mobile robotic applications. By sensor fusion, we can compensate the deficiencies of stand-alone sensors and provide more reliable estimations. We introduce a tightly coupled lidar-IMU fusion method in this paper. By jointly minimizing the cost derived from lidar and IMU measurements, the lidarIMU odometry (LIO) can perform well with considerable drifts after long-term experiment, even in challenging cases where the lidar measurement can be degraded. Besides, to obtain more reliable estimations of the lidar poses, a rotation-constrained refinement algorithm (LIO-mapping) is proposed to further align the lidar poses with the global map. The experiment results demonstrate that the proposed method can estimate the poses of the sensor pair at the IMU update rate with high precision, even under fast motion conditions or with insufficient features.

Metric localization plays a critical role in vision-based navigation. For overcoming the degradation of matching photometry under appearance changes, recent research resorted to introducing geometry constraints of the prior scene structure. In this paper, we present a metric localization method for the monocular camera, using the Signed Distance Field (SDF) as a global map representation. Leveraging the volumetric distance information from SDFs, we aim to relax the assumption of an accurate structure from the local Bundle Adjustment (BA) in previous methods. By tightly coupling the distance factor with temporal visual constraints, our system corrects the odometry drift and jointly optimizes global camera poses with the local structure. We validate the proposed approach on both indoor and outdoor public datasets. Compared to the state-of-the-art methods, it achieves a comparable performance with a minimal sensor configuration.

Multiple LiDARs have progressively emerged on autonomous vehicles for rendering a rich view and dense measurements. However, the lack of precise calibration negatively affects their potential applications. In this paper, we propose a novel system that enables automatic multi-LiDAR calibration method without any calibration target, prior environment information, and manual initialization. Our approach starts with a hand-eye calibration by aligning the motion of each sensor. The initial results are then refined by an appearance-based method by minimizing a cost function constructed by point-plane distance. Experimental results on simulated and real-world data demonstrate the reliability and accuracy of our calibration approach. The proposed approach can calibrate a multi-LiDAR system with the rotation and translation errors less than 0. 04rad and 0. 1m respectively for a mobile platform.

End-to-end visual-based imitation learning has been widely applied in autonomous driving. When deploying the trained visual-based driving policy, a deterministic command is usually directly applied without considering the uncertainty of the input data. Such kind of policies may bring dramatical damage when applied in the real world. In this paper, we follow the recent real-to-sim pipeline by translating the testing world image back to the training domain when using the trained policy. In the translating process, a stochastic generator is used to generate various images stylized under the training domain randomly or directionally. Based on those translated images, the trained uncertainty-aware imitation learning policy would output both the predicted action and the data uncertainty motivated by the aleatoric loss function. Through the uncertainty-aware imitation learning policy, we can easily choose the safest one with the lowest uncertainty among the generated images. Experiments in the Carla navigation benchmark show that our strategy outperforms previous methods, especially in dynamic environments.