Fig. 1. Overview of 3D imaging LiDAR. Rotating mirror reprinted with permission from Ref. [13], copyright 2010, Springer Nature. Galvanometer mirror reprinted with permission from Ref. [26], under a Creative Commons Attribution 4.0 International License. MEMs mirror reprinted with permission from Ref. [27], copyright 2021, Optica Publishing Group. Optical phased array reprinted with permission from Ref. [28], copyright 2020, Optica Publishing Group. Metasurface reprinted with permission from Ref. [25], copyright 2020, Springer Nature. Focal plane switch array reprinted with permission from Ref. [29], copyright 2022, Springer Nature. Spectral scanning LiDAR reprinted with permission from Ref. [30], copyright 2020, Springer Nature. Flash LiDAR reprinted with permission from Ref. [31], copyright 2019, IEEE. Parallel FMCW LiDAR using soliton microcomb reprinted with permission from Ref. [32], copyright 2020, Springer Nature. OCDMA parallel 3D imaging reprinted with permission from Ref. [33], copyright 2020, Optica Publishing Group. Compressed sensing reprinted with permission from Ref. [34], copyright 2019, Optica Publishing Group.

Fig. 2. (a) Basic architecture of a serial detection 3D imaging LiDAR system. The type of light source used in the LiDAR system depends on the ranging technology employed. For pulsed LiDAR, laser diodes or mode-locked fiber lasers can serve as the light source. For AMCW or FMCW LiDAR, laser diodes or other semiconductor lasers capable of transmitting continuous waves can be used. Here modulation devices are employed to generate different signals, although they are not all depicted here. (b) Basic architecture of parallel detection LiDAR, flash LiDAR, for instance. (c) Working principles of different ranging technologies for the serial detection method.

Fig. 3. Mechanical beam scanners. (a) Typical polygonal prism configurations: convergent beam scanning, regular polygonal scanner, pyramidal mirror scanner, and single-faceted cantilevered scanner. (b) Left: basic structure of galvanometric scanner. Right: structure of a magnet torque motor. (c) Left: working principle of beam steering by using a pair of prisms. Right: schematic diagram of applying Risley prism in vehicle LiDAR. (a), (b) Reprinted from Ref. [40], under a Creative Commons Attribution 4.0 International License. (c) Reprinted with permission from Ref. [41], copyright 2018, IEEE.

Fig. 4. MEMS beam scanners. (a) 2D MEMS scanner based on ES actuation. (b) EM MEMS scanner structure with coils and magnets. (c) Basic working principle and schematic of PE MEMS scanner. (d) ET MEMS scanner. (e) Fast MEMS scanner with quasistatic resonant actuation. (a) Reprinted with permission from Ref. [71], copyright 2001, Elsevier. (b), (c), (e) Reprinted from Ref. [66], copyright 2014, IEEE. (d) Reprinted from Ref. [21], under a Creative Commons Attribution 4.0 International License.

Fig. 5. Optical phased array. (a) Working principle of optical phased array (OPA). (b) Schematic diagram of TO and EO phase shifters. Heater metal layer is made of aluminum or other metals. (c) Picture of 512-element optical phased array with an inline architecture and the 3D imaging results. (d) Architecture of FMCW LiDAR system for long range detection and its one-dimensional scanning ranging results. (e) OPA device and the packaged system with epoxied fiber of the first OPA-based coherent LiDAR system from the group at MIT. (b) Reprinted with permission from Ref. [84], copyright 2021, IEEE. (c) Reprinted with permission from Ref. [85], copyright 2019, IEEE. (d) Reprinted with permission from Ref. [86], copyright 2018, IEEE. (e) Reprinted with permission from Ref. [87], copyright 2017, Optica Publishing Group.

Fig. 6. Focal plane switch array. (a) Working principle of FPSA for beam steering. (b) Microscopic images of a large-scale FPSA beam scanner. (c) Schematic diagram of an integrated FPSA chip and (d) velocity measurements by the FPSA LiDAR. (b) Reprinted with permission from Ref. [29], copyright 2022, Springer Nature. (c), (d) Reprinted with permission from Ref. [97], copyright 2021, Springer Nature.

Fig. 7. Slow-light grating beam scanner. (a) Basic structure and working principle of slow-light grating beam scanner in Ref. [104]. (b) Schematic diagram of the on-chip LiDAR. (c) Point cloud images of two different scenes. (a) Reprinted with permission from Ref. [104], copyright 2017, Optica Publishing Group. (b), (c) Reprinted with permission from Ref. [105], copyright 2022, IEEE.

Fig. 8. Metasurface-based beam scanner. (a) Beam-steering performance operated by the TCO material approach. (b) Schematic diagram of MQW metasurface. (c) Liquid-crystal-based SLMs for beam steering. (d) Illustration of active metasurface array for beam steering. (e) Schematic diagram of the 3D LiDAR experimental setup and the 3D depth image produced using the metaphotonic SLM. (a) Reprinted with permission from Ref. [111], copyright 2016, American Chemical Society. (b) Reprinted with permission from Ref. [112], copyright 2015, Wiley-VCH. (c) Reprinted with permission from Ref. [113], copyright 2019, AAAS. (d), (e) Reprinted with permission from Ref. [25], copyright 2020, Springer Nature.

Fig. 9. DMD-based beam scanner. (a) System setup, true scene, and depth resolved images of the proposed LiDAR system. Recoding of 3D imaging results at a 5 Hz frame rate. (b) Top: illustration of the optical system and beam-steering scheme. Bottom: representation of a captured movie of the LiDAR system capturing swinging pendulums placed in each of the five scanning diffraction orders. (a) Reprinted with permission from Ref. [117], copyright 2021, AAAS. (b) Reprinted with permission from Ref. [118], copyright 2017, Optica Publishing Group.

Fig. 10. (a) Top: typical configuration of an acousto-optical deflector. Bottom: two-dimensional scanner formed by arranging two OADs orthogonally in series. (b) Top: schematic of frequency-angular resolving LiDAR using acousto-optic beam scanner. Bottom left: photograph of an LNOI chip with 10 acousto-optic beam scanners. Bottom right: LiDAR imaging results and the beating signal. (a) Reprinted with permission from Ref. [119], copyright 2014, Elsevier. (b) Reprinted with permission from Ref. [120], copyright 2023, Springer Nature.

Fig. 11. (a) Illustration of the principle and anti-interference ability of spectral scanning LiDAR. (b) Basic architecture of spectral scanning pulsed LiDAR system. A spectro-temporal modulated laser source was used. (c) Virtual imaged phased array (VIPA)-based 2D disperser for 2D spectral scanning LiDAR. (d) Top: experimental setup of diffractive element for 2D spectral scanning. Bottom: microscopy image of two DOEs. (e) 1D (top) and 2D (bottom) spectral scanning performance. (f) Fast 3D imaging FMCW LiDAR system. Top: schematic diagram of swept-source FMCW LiDAR system. Short-time Fourier transform (STFT) is used for data processing. Bottom: video-rate 3D imaging results of human hands. (b) Reprinted with permission from Ref. [30], copyright 2020, Springer Nature. (c) Reprinted with permission from Ref. [125], copyright 2021, Optica Publishing Group. (d), (e) Reprinted with permission from Ref. [126], copyright 2021, Optica Publishing Group. (f) Reprinted with permission from Ref. [127], copyright 2022, Springer Nature.

Fig. 12. (a) SPAD sensor for flash LiDAR and the imaging results. (b) Three types of multiple-channel scanning LiDAR systems from commercial products, Velodyne LiDAR (Ultra Puck), Ouster (OS2), and Robosense (RS-Ruby). (c) Structure of proposed Risley-prism-based multi-beam scanning LiDAR. Experimental setup and working principle of multiple-channel scanning. (d) Working principle of parallel FMCW LiDAR using soliton microcomb. (a) Reprinted with permission from Ref. [31], copyright 2019, IEEE. (b) Provided by Refs. [135137" target="_self" style="display: inline;">–137]. (c) Reprinted with permission from Ref. [138], copyright 2022, SPIE. (d) Reprinted with permission from Ref. [32], copyright 2020, Springer Nature.

Fig. 13. (a) System architecture of compressive FMCW-LiDAR depth mapping. (b) Top: system structure of all-optical spectro-temporal encoding LiDAR system. Middle: illumination and echo signal of a serial LiDAR. Bottom: correlated spectro-temporal encoding enables parallelism and speeds up the LiDAR M-times beyond the time-of-flight limit. (a) Reprinted with permission from Ref. [34], copyright 2018, Optica Publishing Group. (b) Reprinted with permission from Ref. [152], copyright 2022, American Institute of Physics.