LICFusion: LiDARInertialCamera Odometry This paper presents a tightlycoupled multisensor fusion algorithm termed LiDARinertialcamera fusion (LICFusion), which efficiently fuses IMU measurements, sparse visual features, and extracted LiDAR points. In particular, the proposed LICFusion performs online spatial and temporal sensor calibration between all three asynchronous sensors, in order to compensate for possible calibration variations. The key contribution is the optimal (up to linearization errors) multimodal sensor fusion of detected and tracked sparse edgesurf feature points from LiDAR scans within an efficient MSCKFbased framework, alongside sparse visual feature observations and IMU readings. We perform extensive experiments in both indoor and outdoor environments, showing that the proposed LICFusion outperforms the stateoftheart visualinertial odometry (VIO) and LiDAR odometry methods in terms of estimation accuracy and robustness to aggressive motions.

RollingShutter Modelling for Direct VisualInertial Odometry We present a direct visualinertial odometry (VIO) method which estimates the motion of the sensor setup and sparse 3D geometry of the environment based on measurements from a rollingshutter camera and an inertial measurement unit (IMU). The visual part of the system performs a photometric bundle adjustment on a sparse set of points. This direct approach does not extract feature points and is able to track not only corners, but any pixels with sufficient gradient magnitude. Neglecting rollingshutter effects in the visual part severely degrades accuracy and robustness of the system. In this paper, we incorporate a rollingshutter model into the photometric bundle adjustment that estimates a set of recent keyframe poses and the inverse depth of a sparse set of points. IMU information is accumulated between several frames using measurement preintegration, and is inserted into the optimization as an additional constraint between selected keyframes. For every keyframe we estimate not only the pose but also velocity and biases to correct the IMU measurements. Unlike systems with globalshutter cameras, we use both IMU measurements and rollingshutter effects of the camera to estimate velocity and biases for every state. Last, we evaluate our system on a new dataset that contains globalshutter and rollingshutter images, IMU data and groundtruth poses for ten different sequences, which we make publicly available. Evaluation shows that the proposed method outperforms a system where rolling shutter is not modelled and achieves similar accuracy to the globalshutter method on globalshutter data.