BUYER GUIDE

Robot LiDAR Selection: A Practical Sourcing Guide

Robot LiDAR selection should begin with the perception job: localization, obstacle detection, mapping, docking, people detection or a combination of these. Maximum range is rarely the only limiting factor. Close-range blind zones, vertical coverage, point distribution, target reflectivity, motion distortion, timing and mounting height can determine whether a sensor sees the hazards and landmarks that matter. Describe the operating environment and perception stack before requesting models. Ask suppliers for data from representative targets and trajectories, plus the exact synchronization, power, network, firmware and SDK configuration needed for evaluation.

How to shortlist

Perception task and geometry

Define whether the sensor supports SLAM, obstacle detection, mapping, docking or object perception. Derive horizontal and vertical coverage from mounting position and required detection zones.

Blind zone and minimum useful range

Check the sensor's guaranteed close-range performance against bumpers, forks, robot bodywork and hazards near the floor, not only whether returns can sometimes be recorded.

Range by target reflectivity

Compare required range using representative dark, matte, reflective and small targets under the expected light and weather conditions.

Point distribution

Evaluate point rate, angular precision, scan pattern, frame rate and vertical sampling at target distance. Simulate whether narrow objects and edges receive enough usable returns.

Motion and synchronization

Specify robot speed and angular rate, timestamp accuracy, PTP or other synchronization, IMU use and whether deskewing is handled in the sensor, driver or perception stack.

Interference and false returns

Ask about direct sunlight, other LiDARs, reflective surfaces, fog, dust, rain and glass. Define how invalid or low-confidence returns are exposed to software.

Mechanical, electrical and environmental fit

Check dimensions, mass, connector orientation, vibration, ingress rating, temperature, average and startup power, supply range, heat dissipation and protective-window requirements.

Driver and lifecycle support

Review packet format, Ethernet requirements, ROS and native SDK support, firmware compatibility, calibration data, diagnostics, recorded datasets and replacement compatibility.

Matching catalog models

31 models

WanJi32L-LiDAR

Mechanical multi-line lidar32

Inquiry

Model	Type	Range	Channels	Point rate	Field of view	Range accuracy	IP rating	Weight		Quote
WanJi32L-LiDAR	Mechanical multi-line lidar^†	—	32^†	—	—	—	—	—	Inquiry	Quote
WanJi16L-LiDAR	Mechanical multi-line lidar^†	—	16^†	—	—	—	—	—	Inquiry	Quote
WanJi8L-LiDAR	Mechanical multi-line lidar^†	—	8^†	—	—	—	—	—	Inquiry	Quote
ZVISIONZVISION EZ5	SPAD-based solid-state lidar^†	—	—	—	—	—	—	—	Inquiry	Quote
ZVISIONZVISION EZ6	SPAD-based solid-state lidar^†	—	—	—	—	—	—	—	Inquiry	Quote
ZVISIONZVISION NZ1	Wide-angle 3D lidar^†	—	—	—	—	—	—	—	Inquiry	Quote
SLAMTECRPLIDAR S2E	ToF 2D SLAM lidar^†	—	—	—	—	—	IP65^†	—	Inquiry	Quote
SLAMTECRPLIDAR S2	ToF 2D SLAM lidar^†	0.05-30 m (90% reflectivity); up to 50 m on S2P^†	—	—	—	—	IP65^†	—	Inquiry	Quote
SLAMTECRPLIDAR A3	Triangulation 2D SLAM lidar^†	25 m (white target, enhanced mode) / 20 m outdoor mode^†	—	—	—	1% (<=3 m), 2% (3-5 m), 2.5% (5-25 m)^†	—	190 g^†	Inquiry	Quote
BenewakeAD2-S-X3	Semi-solid-state automotive lidar^†	300 m @10% reflectivity^†	256^†	—	—	—	IP67 & IP6K9K^†	1.2 kg^†	Inquiry	Quote
BenewakeTF03	Single-point ToF lidar^†	0.1-180 m^†	—	—	—	—	IP67^†	86 +/-3 g^†	Inquiry	Quote
LSLIDARCH16R	Hybrid solid-state blind-spot lidar^†	—	16^†	—	—	—	—	—	Inquiry	Quote